Lotka Volterra. Stability Analysis of Delayed Periodic Lotka Volterra Systems
|
|
- Χαρικλώ Λαμπρόπουλος
- 6 χρόνια πριν
- Προβολές:
Transcript
1 Lotka Volterra Stability Analysis of Delayed Periodic Lotka Volterra Systems 25 4
2 ii iii
3 Lotka Volterra 2 2 Lotka Volterra Lotka Volterra Lotka Volterra Lotka Volterra Lotka Volterra Schauder Lotka Volterra Lyapunov Lyapunov Lotka Volterra M O175.1, Q141.
4 Abstract The Lotka Volterra system is one of the most important models in mathematical ecology. Since it was proposed in the 192 s, successful applications of this model and its variants have been widely found in diverse areas of natural and social sciences, which have provided powerful tools for understanding the mechanisms of competition and dynamical evolution in a complex system. In recent years, the delayed periodic Lotka Volterra systems, which introduce the time delays and periodic oscillations into the original model, have attracted increasing interest. This thesis focus on the delayed periodic Lotka Volterra systems of a very general form. Using fundamental and straightforward analytic techniques, we discuss in detail the issues on positive and nonnegative periodic solutions that have significant ecological meanings. Several new results of existence and stability of periodic solutions are proved. This thesis is organized as follows. Chapter 1: Introduction. We briefly introduce the applications of Lotka Volterra systems in ecology, neural networks and other fields, and the necessity of research on delayed periodic Lotka Volterra systems. Model descriptions are then given, and challenging problems are proposed based on analyzing the ideas and methods currently used to investigate the model. Chapter 2: Existence and stability of positive periodic solutions. Positive periodic solutions of Lotka Volterra systems imply that all the species in an ecosystem coexist permanently with periodic oscillations. Taking a different way from those in the literature and applying Schauder s fixed point theorem, we prove some existence conditions for positive periodic solutions. And the global asymptotic stability and global exponential stability are also addressed. Chapter 3: Stability of nonnegative periodic solutions. The vanishing components of a stable periodic solution imply that the corresponding species are driven to extinction eventually. To study the stability of nonnegative periodic solutions, we first discuss the boundedness of solutions, and then prove the stability theorems by simple mathematical analysis. By using this approach, we avoid the possible difficulty in applying the Lyapunov s method.
5 iv Chapter 4: A numerical example and discussions. The comprehensive example is carefully designed and intended both to illustrate how to verify the stability criteria obtained in previous chapters, and to explain the distinction between the conditions for positive and nonnegative periodic solutions. All these results provide insights into the understanding of dynamical behavior of complex ecosystems. Keywords: Lotka Volterra system, neural network, delay, periodic solution, global asymptotic stability, global exponential stability, nonsingular M-matrix.
6 1.1 (trophic web) (1) (2) ( ) (3) ( ) (predator), (prey), Volterra ]. dn = N(a bp ), dt (1.1) dp dt = P (cn d), N(t) P (t) t a, b, c, d 192 Lotka 12] Lotka Volterra (). (1.1) n ( ) dx i dt = x i b i a ij x j, i = 1, 2,..., n, x i (t) i t b i i a ii > a ij (i j) Lotka Volterra ( ) 5, 19].
7 2 14, 17, 18, 22] Fukai Tanaka Lotka Volterra 7]. Asai MOS 2]. (recurrent) Lotka Volterra 17]. Lotka Volterra Lotka Volterra 8, 9, 21, 23] 2 9 Lotka Volterra Hopfield 1, 13, 32]. Lotka Volterra 1.2 n Lotka Volterra
8 3 dx i (t) = x i (t) b i (t) dt a ij (t)x j (t) x j (t s)d s µ ij (t, s) ] i = 1, 2,..., n,, (1.2) b i (t) a ij (t) ω > a ii (t) >, t, d s µ ij (t, s) d s µ ij (t + ω, s) = d s µ ij (t, s), t K ij (s) d s µ ij (t, s) dk ij (s) dk ij (s) < +, s dk ij (s) < + (1.3) i, j = 1, 2,..., n x i (s) = φ i (s), s (, ], (1.4) φ i (s) s φ i (s) >. d s µ ij (t, s) = b ij (t)δ(s + τ ij )ds + c ij (t, s)ds, δ( ) Dirac δ i, j = 1, 2,..., n, b ij (t) τ ij (t) ω c ij (t + ω, s) = c ij (t, s), τ ij (t), (1.2) dx i (t) = x i (t) b i (t) a ij (t)x j (t) b ij (t)x j (t τ ij (t)) dt ] c ij (t, s)x j (t s)ds, i = 1, 2,..., n. (1.5) (1.2), ) (1.2) (1.4) 1 (1.2) (1.4), ) (1.2) t log x i (t) log x i () = b i (u) a ij (u)x j (u) x j (u s)d s µ ij (u, s) t log x i (t) >, t > i = 1, 2,..., n x i (t) >. ] du.
9 4 1 1, 1 (1.2) (1.4) (1.2) (1.2) 2 x (t) (1.2) x(t) lim x(t) t x (t) =, R n x (t) 3 x(t) x (t) (1.2) ε >, lim x(t) t x (t) = O(e εt ), R n x (t) ε 4 (1.2) i = 1, 2,..., n ω b i(u)du >, i j a ij (t), i, j = 1, 2,..., n d s µ ij (t, s), 5 (1.2) i = 1, 2,..., n ω b i(u)du >, i j a ij (t), i, j = 1, 2,..., n d s µ ij (t, s), Lotka Volterra Lotka Volterra 6,1,11,24 26,28,3]. Lotka Volterra (), (1.2)
10 5 Lotka Volterra (permanence), (1.2) Schauder Lotka Volterra Lotka Volterra 15, 16, 27]. Lotka Volterra Lotka Volterra Lyapunov ( ) Lyapunov Lyapunov 1.3 M 4] 5 M 4]. 2 C = (c ij ) R n n, C M
11 6 (a) C (b) C T M (c) C C (d) ξ = (ξ 1, ξ 2,..., ξ n ) T Cξ (e) D CD + DC T (f) C 1 (b) (d) () 3 C = (c ij ) R n n, (a) ξ 1, ξ 2,..., ξ n n ξ jc ij > i = 1, 2,..., n (b) η 1, η 2,..., η n n η ic ji > i = 1, 2,..., n (1.2) 6 f : R + R H t > f(t), {s i, t i ]}, s i >, s i, t i ] s j, t j ] =, t i s i = t j s j > (i j), 2 i=1 ti s i f(u)du = +. a >, t > f(t) > a, f H. (1.2) 4 {f n }, ω] ω >, ω lim n f n (t) dt =, (1.6) f n, ω] ε >, k, t k, ω] n k > k f nk (t k ) ε. {f n } δ >
12 7 t k δ t t k + δ ω (1.6) f nk (t) ε 2. f nk (t) dt δ ε 2 >.
13 2.1 Schauder (1.2) f + = max{, f}, f = min{, f}, f] m = sup t f(t), f] l = inf t f(t). 1 ξ 1, ξ 2,..., ξ n, b i (t) ξ i a ii (t) ξ j a ij (t) ξ j d s µ ij (t, s)] <, t >, (2.1) ω {, j i b i (u) ξ j a + ij (u) ξ j d s µ ij (u, s)] }du + >, (2.2), j i i = 1, 2,..., n (1.2) ω C = C((, ], R n ) Banach φ = max i sup <θ φ i (θ). η ω η < min i ξ = max i ξ i, γ 1 = max i γ 2 = max i, j γ 3 = max i, j sup b i (t), t sup t sup t a ij (t), d s µ ij (t, s), K = exp{γ 1 + (γ 2 + γ 3 )ξ]ω}, L = ξγ 1 + (γ 2 + γ 3 )ξ], { b i (u) n, j i ξ ja + ij (u) n ξ j K ω a ii(u)du } d sµ ij (u, s)] + du, ξ i. (2.3)
14 9 Ω = {x(θ) C : η x i (θ) ξ i (i = 1, 2,..., n), ẋ(θ) L}, Ω C Ω C T T : φ(θ) x(θ + ω, φ), x(t) = x(t, φ) (1.2) x i (θ) = φ i (θ) ( < θ ) Schauder T Ω Ω, φ Ω, x Ω. t i x i (t ) = ξ i, x i (t) ξ i, t < t, j i x j (t) ξ j, t t. (2.1) d log x i (t) dt = b i (t ) a i j(t )x j (t ) x j (t s)d s µ i j(t, s) t=t b i (t ) ξ i a i i (t ) ξ j a i j (t ) ξ j d s µ i j(t, s)], j i <. < t ω i = 1, 2,..., n x i (t) ξ i. x i (t) η < t ω i = 1, 2,..., n < t 1 ω i 1 x i1 (t 1 ) < η, < s ω { t1 } d log x i1 (u) x i1 (t 1 s) = x i1 (t 1 ) exp du < Kη. du (2.3) t1 { d log x i1 (u) t1 du = b i1 (u) t 1 ω du t 1 ω { ω >. Kη b i1 (u) ω t 1 s a i1 j(u)x j (u), j i 1 ξ j a + i 1 j (u) a i1 i 1 (u)du x j (u s)d s µ i1 j(u, s) ξ j d s µ i1 j(u, s)] }du + } du
15 1 { t1 x i1 (t 1 ) = x i1 (t 1 ω) exp t 1 ω } d log x i1 (u) du > η. du x i1 (t 1 ) < η < t ω i = 1, 2,..., n x i (t) η. ẋ(θ + ω) L. T Ω Ω. Schauder φ Ω T φ = φ. x(t, φ ) = x(t, T φ ), x(t, φ ) = x(t + ω, φ ). x(t, φ ) (1.2) ω 3 x (t) η x i (t) ξ i, i = 1, 2,..., n. dx i (t) ] σij = x i (t) b i (t) a ij (t)x j (t) x j (t s)d s µ ij (t, s), dt i = 1, 2,..., n, (2.4) σ ij > 1 2 ξ 1, ξ 2,..., ξ n, b i (t) ξ i a ii (t) ω { b i (u), j i, j i ξ j a ij (t) ξ j d s µ ij (t, s)] <, t >, ξ j a + ij (u), j i ξ j d s µ ij (u, s)] }du + >, i = 1, 2,..., n (2.4) ω 1 < t ω i = 1, 2,..., n x i (t) η, σ = max σ ij, i, j K 1 = exp{γ 1 + (γ 2 + γ 3 )ξ](ω + σ)},
16 11 < s ω + σ { x i1 (t 1 s) = x i1 (t 1 ) exp t1 t 1 s } d log x i1 (u) du < K 1 η. du t1 d log x i1 (u) ω { σi1 du b i1 (u) ξ j a + i t 1 ω du 1 j (u) j ξ j d s µ i1 j(u, s)] }du +, j i 1, j i 1 { ω σi1 } i 1 K 1 η a i1 i 1 (u) + d s µ i1 i 1 (u, s)] ]du +. η { ω (1.2) b i (u), j i ] bj a ij (u) a jj m i = 1, 2,..., n ω ] } bj d s µ ij (u, s) du > (2.5) a jj m ( 4), 1 (2.1) (2.2) ξ i ω b i (t) ξ i a ii (t), t >, { } b i (u) ξ j a ij (u) ξ j d s µ ij (u, s) du >., j i = b i /a ii ] m (i = 1, 2,..., n), 1 2 (1.2) ζ 1, ζ 2,..., ζ n ζ i a ii (t) + ζ j a ij (t) + ζ j d s µ ij (t, s) > (2.6), j i t > i = 1, 2,..., n (1.2) ω
17 12 ( 5), 1 (2.2) ω (2.1) b i (t) ξ i a ii (t) a, j i a > max sup i t ξ j a ij (t) b i (u)du >, ξ j d s µ ij (t, s) <, t >. (2.7) b i (t) ζ i a ii (t) + n, j i ζ ja ij (t) + n ζ j d s µ ij (t, s), ξ i = aζ i (i = 1, 2,..., n), (2.7) ] Lotka Volterra dx i (t) dt = x i (t) b i (t) a ii (t)x j (t) σij b ij (t)x j (t τ ij (t)) c ij (t, s)x j (t s)ds ], i = 1, 2,..., n, (2.8) σ ij > (2.4) Lyapunov Razumikhim 1 ξ 1, ξ 2,..., ξ n α, b i (t) ξ i a ii (t) ω { σij (1 + α)ξ j b ij (t) (1 + α)ξ j b i (u), j i ξ j b + ij (t), j i c ij (t, s)ds, t >, σij ξ j c + ij }du (t, s)ds >, i = 1, 2,..., n (2.8) ω
18 13 (2.8) 2 25] 1 1 1] dx i (t) ] Tij = x i (t) b i (t) a ii (t)x j (t) a ij (t) K ij (s)x j (t s)ds, dt, j i i = 1, 2,..., n, (2.9) T ij > T ij K ij (s)ds = 1 (i j). 2 f = 1 ω ω f(u)du. a ij e y j = b i, i = 1, 2,..., n (2.1) (y 1, y 2,..., y n) T R n, b i, j i ] bj a ij > (2.11) a jj m i = 1, 2,..., n (2.9) ω 1 (2.5) (2.9) (2.11) 1, 2 (2.1) 11] dx i (t) = x i (t) b i (t) a ii (t)x i (t) + a ij (t)x j (t) dt, j i ] τj + b ij (t) x j (t s)dµ j (s), i = 1, 2,..., n, (2.12) τ j > µ j (τ j +) µ j ( ) = 1 (j = 1, 2,..., n). 3 f = 1 ω b i 2a ii e y i + ω f(u)du. (a ij + b ij )e y j =, i = 1, 2,..., n (2.13)
19 14 (y1, y2,..., yn) T R n, { } a ii ] l a ji + b ji ] m + b ii ] m > (2.14), j i i = 1, 2,..., n (2.12) ω (2.14) 3 ζ 1, ζ 2,..., ζ n { } ζ i a ii ] l ζ j a ij + b ij ] m + ζ i b ii ] m >, j i i = 1, 2,..., n 2 (2.6) 2, 3 (2.13) 2.3 (1.5) s c ij (t, s) ds < +, (2.15) τ ij (t) τ ij (t) < 1, ψ ij (t) = t τ ij (t) ψ 1 ij (t) (i, j = 1, 2,..., n). τ ij t ψ 1 ij (t) = t + τ ij (1.5) i = 1, 2,..., n ω b i(u)du >, i j a ij (t), i, j = 1, 2,..., n b ij (t) c ij (t, s), 8 (1.5) i = 1, 2,..., n ω b i(u)du >, i j a ij (t), i, j = 1, 2,..., n b ij (t) c ij (t, s), (1.5) ω 3 β i (t) = ζ i a ii (t) (1.5) ω x (t), ζ 1, ζ 2,..., ζ n, j i ζ j a ji (t) ζ j b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t)) ζ j c ji (t+s, s) ds H (2.16)
20 15 i = 1, 2,..., n x (t) ε > p > log x i (t) log x i (t) p x i (t) x i (t) (2.17) ζ i (a ii (t) εp), j i ζ j a ji (t) ζ j b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t)eε(ψ 1 ji (t) t) ζ j c ji (t + s, s) e εs ds (2.18) t > i = 1, 2,..., n x (t) ε. w i (t) = x i (t) x i (t), z i (t) = log x i (t) log x i (t) (i = 1, 2,..., n), L(t) = ζ i z i (t) + i=1 + ζ i i, t ζ i i, t t s t τ ij (t) b ij (ψ 1 ij (s)) 1 τ ij (ψ 1 ij (s)) w j(s) ds c ij (θ + s, s) w j (θ) dθds. (2.15) L() L(t) (1.5) dl(t) = ζ i sign(z i (t)) a ii (t)w i (t) a ij (t)w j (t) b ij (t)w j (t τ ij (t)) dt i=1, j i ] bij (ψ 1 ij c ij (t, s)w j (t s)ds + ζ (t)) i 1 τ i, ij (ψ 1 ij (t)) w j(t) ] b ij (t) w j (t τ ij (t)) + ζ i c ij (t + s, s) w j (t) ds = c ij (t, s) w j (t s) ds ζ i a ii (t) i=1, j i i, ] ζ j a ji (t) ] ζ j c ji (t + s, s) ds w i (t) β i (t) w i (t). i=1 ζ j b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t))
21 16 + dl(t) dt i=1 β i (t) w i (t) dt L() < +. (2.19) L(t) x(t) (1.5) x(t), ) β i (t) H, (2.19) x (t) lim t x(t) x (t) =. i=1 L 1 (t) = ζ i z i (t) e εt + i=1 + ζ i i, i, t t s t ζ i L 1 (t) (1.5) t τ ij (t) b ij (ψ 1 ij (s)) 1 τ ij (ψ 1 ij (s)) w j(s) e εψ 1 ij (s) ds c ij (θ + s, s) w j (θ) e ε(θ+s) dθds. dl 1 (t) dt = ζ i sign(z i (t))e εz εt i (t) a ii (t)w i (t) i=1 + + b ij (t)w j (t τ ij (t)) e εt. i,, j i a ij (t)w j (t) c ij (t, s)w j (t s)ds bij (ψ 1 ζ i e εt ij (t)) ] 1 τ ij (ψ 1 ij (t)) w j(t) e ε(ψ 1 ij (t) t) b ij (t) w j (t τ ij (t)) ] ζ i e εt c ij (t + s, s) w j (t) e εs ds c ij (t, s) w j (t s) ds i, ζ i (a ii (t) εp) i=1, j i ] ζ j c ji (t + s, s) e εs ds w i (t) ζ j a ji (t) ζ j ] b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t))eε(ψ 1 ji (t) t) L 1 (t) L 1 (t) ζ i log x i (t) log x i (t) L 1 ()e εt. (2.2) i=1
22 17 x (t) m 1 m 2 m 1 x i (t) m 2 t > i = 1, 2,..., n q x i (t) x i (t) q log x i (t) log x i (t) (2.21) t > i = 1, 2,..., n (2.2) (2.21) ζ i x i (t) x i (t) ql 1 ()e εt. (2.22) i=1 x {ζ,1} = (2.22) ζ i x i, i=1 x(t) x (t) {ζ,1} = O(e εt ). x (t) ε. 4 x (t) m 1 m 2 m 1 x i (t) m 2 t > i = 1, 2,..., n (2.17) p 3, 25, 26] (2.16) ξ 1, ξ 2,..., ξ n, ζ 1, ζ 2,..., ζ n b i (t) ξ i a ii (t) ξ j a ij (t) ξ j b ij (t) ξ j c ij (t, s)ds <, t >, ω ζ i a ii (t), j i b i (u), j i, j i ζ j a ji (t) ξ j a + ij (u) ξ j b + ij (u) ξ j c + ij ]du (u, s) >, ζ j b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t)) ζ j c ji (t + s, s) ds H i = 1, 2,..., n (1.5) ω x (t). ε > p > log x i (t) log x i (t) p x i (t) x i (t)
23 18 ζ i (a ii (t) εp), j i ζ j a ji (t) ζ j b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t)eε(ψ 1 ji (t) t) ζ j c ji (t + s, s) e εs ds t > i = 1, 2,..., n x (t) ε. { ω 3 b i (u) ζ i a ii (t) (1.5) ζ 1, ζ 2,..., ζ n, j i, j i ] bj a ij (u) a jj m ζ j a ji (t) ζ j ] bj b ij (u) a jj m b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t)) ] } bj c ij (u, s)ds du >, a jj m ζ j c ji (t + s, s) ds H i = 1, 2,..., n (1.5) ω x (t). ε > p > log x i (t) log x i (t) p x i (t) x i (t) ζ i (a ii (t) εp), j i ζ j a ji (t) ζ j b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t)eε(ψ 1 ji (t) t) ζ j c ji (t + s, s) e εs ds t > i = 1, 2,..., n x (t) ε ξ i a ii (t) + ζ i a ii (t) (1.5) ξ 1, ξ 2,..., ξ n, ζ 1, ζ 2,..., ζ n, j i, j i ξ j a ij (t) + ζ j a ji (t) ξ j b ij (t) + ζ j ξ j c ij (t, s)ds >, t >, b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t)) ζ j c ji (t + s, s) ds H
24 19 i = 1, 2,..., n (1.5) ω x (t). ε > p > ζ i (a ii (t) εp) log x i (t) log x i (t) p x i (t) x i (t), j i ζ j a ji (t) ζ j b ji (ψ 1 ji (t)) 1 τ ji (ψ 1 ji (t)eε(ψ 1 ji (t) t) ζ j c ji (t + s, s) e εs ds t > i = 1, 2,..., n x (t) ε. 2 3
25 3.1 Lotka Volterra (1.2) 5 ξ 1, ξ 2,..., ξ n ξ i a ii (t) + ξ j a ij (t) + ξ j d s µ ij (t, s)] > (3.1), j i t < ω i = 1, 2,..., n (1.2) x(t) M i x i (t) M i t > i = 1, 2,..., n x(t) α i = inf t { ξ i a ii (t) +, j i H = max i { λ > max i } ξ j a ij (t) + ξ j d s µ ij (t, s)] >, { } sups φ i (s), sup t<ω ξ i b + i (t) α i M i = λξ i (i = 1, 2,..., n). φ i (s) Hξ i λξ i = M i s t i ], H x i (t ) = M i, x i (t) M i, t < t, }, j i x j (t) M j, t t.
26 21 d log x i (t) dt = b i (t ) a i j(t )x j (t ) x j (t s)d s µ i j(t, s) t=t b i (t ) a i i (t )M i a i j (t )M j M j d s µ i j(t, s)], j i { } = b i (t ) λ ξ i a i i (t ) + ξ j a i j (t ) + ξ j d s µ i j(t, s)], j i b i (t ) λα i <, t > i = 1, 2,..., n x i (t) M i. x(t) (1.2) x(t) x(t) 5 x(t) M i H, x(t) 31] 3.2 (1.2) (1.2) ω 6 (1.2) η 1, η 2,..., η n η i a ii (t) η j a ji (t) η j dk ji (s) > (3.2), j i t < ω i = 1, 2,..., n (1.2) ω w i (t) = x i (t+ω) x i (t), z i (t) = log x i (t+ω) log x i (t) (i = 1, 2,..., n), L(t) = η i z i (t) + η i t i=1 i, t s w j (θ) dθ dk ij (s).
27 22 (2.15) L() { } β = min inf η i a ii (t) η j a ji (t) η j dk ji (s) >. i t, j i L(t) (1.2) dl(t) = η i sign(z i (t)) a ii (t)w i (t) dt i=1 + η i w j (t) dk ij (s) i, η i a ii (t) w i (t) + i=1 + =, j i η i w j (t) dk ij (s) i, η i a ii (t) i=1 β w(t),, j i η j a ji (t), j i a ij (t)w j (t) w j (t s) dk ij (s) a ij (t) w j (t) + w j (t s) dk ij (s) ] w j (t s)d s µ ij (t, s) w j (t s) dk ij (s) ] ] η j dk ji (s) w i (t) w(t) = n i=1 w i(t). + L(t) w(t) n=1 ω w(t) dt 1 L() < +. β x(t + nω) x(t + (n 1)ω) dt < +. Cauchy x(t + nω) L 1, ω] x(t) (1.2) x(t) {x(t + nω)} Arzéla Ascoli {x(t + n k ω)} R x (t). x (t) {x(t + nω)} L 1 ω lim x(t + nω) x (t) dt =. n f n (t) = x(t + nω) x (t). 4 f n (t), ω] x(t + nω) x (t), ω] x(t + nω) x (t) R ] ]
28 23 x (t) (1.2) ω x (t + ω) = lim n x ( t + (n + 1)ω ) = lim n x(t + nω) = x (t), x (t) ω (1.2) x nk (t) x(t), x nk (t) i x n k i dx n k i (t) dt = x n k i (t) b i (t) a ij (t)x n k j (t), (t) x n k j (t s)d sµ ij (t, s) ] i = 1, 2,..., n. k (1.3) {x nk (t)} R x i (t) b i (t) dx i (t) = x i (t) b i (t) dt a ij (t)x j(t) a ij (t)x j(t) x (t) (1.2). t = t + nω, t < ω, x j(t s)d s µ ij (t, s) x j(t s)d s µ ij (t, s) x(t) x (t) = x(t + nω) x (t ). {x(t + nω)}, ω] ] ]., i = 1, 2,..., n, lim x(t) t x (t) =. (3.3) (1.2) x (t). y(t) w i (t) = y i (t) x i (t) z i (t) = log y i (t) log x i (t) (i = 1, 2,..., n). x(t) y(t) (3.3) y(t) x(t) dt < +. lim y(t) x(t) =. t, lim y(t) t x (t) =.
29 24 ( 5) (3.1) (3.2), M a ii = inf t a ii (t) a ij = sup t a ij (t) (i j). 3 7 ξ 1, ξ 2,..., ξ n ξ i a ii ξ j a ij ξ j dk ij (s) > (3.4), j i i = 1, 2,..., n (1.2) ω (3.4) (3.1) 5 (1.2) a ii C = (c ij ) = a ij dk ii (s), i = j,, j i dk ij (s), i j. 3 (3.4) η 1, η 2,..., η n η i a ii η j a ji η j dk ji (s) > (3.5) i = 1, 2,..., n (3.2) 6 ξ 1, ξ 2,..., ξ n (3.4) ξ i = 1 (i = 1, 2,..., n), (3.4) ( 1). 7 2, 5 (3.5) C M (1.2) ω M 2 (a), M
30 25 6 Gauss 2] 3 3 2]
31 4.1 (1.2) 1 Lotka Volterra dx 1 (t) = x 1 (t)7 + sin t (6 + sin t)x 1 (t) (2 + cos t)x 2 (t 1) + (1 + sin t)x 3 (t 1)], dt dx 2 (t) = x 2 (t)4 + cos t (1 + sin t)x 1 (t 1) (5 + sin t)x 2 (t) + (2 + cos t)x 3 (t 1)], dt dx 3 (t) = x 3 (t)1 + sin t (3 + cos t)x 1 (t 1) + (1 + cos t)x 2 (t 1) (9 + cos t)x 3 (t)]. dt (3.5) C = C (3.4) ξ i = 1 (i = 1, 2,..., n) >, = 14 >, = 6 > (a) C M 5 1 ω Matlab dde t 2π 4.3
32 x i t π x(s) (1,.5,.5) T.
33 x x x x 3 = 2π x(s) (1,.5,.5) T, x(s) (.5, 1,.5) T x(s) (.5,.5, 1) T. x 3 = 1 (b 1 (t), b 2 (t), b 3 (t)) (7+sin t, 4+cos t, 4+sin t), (7+sin t, 1+cos t, 1+sin t) ( 1+sin t, 1+ cos t, 1 + sin t), (1.2) ω , 8 (1.2) ξ 1, ξ 2,..., ξ n ξ i a ii ξ j a ij ξ j dk ij (s) > (4.1), j i i = 1, 2,..., n ω
34 29 x i x i (a) (b) (c) t t.8.6 x i t (b 1 (t), b 2 (t), b 3 (t)) (a) (7 + sin t, 4 + cos t, 4 + sin t), (b) (7 + sin t, 1 + cos t, 1 + sin t), (c) ( 1 + sin t, 1 + cos t, 1 + sin t).
35 3 (4.1) 2 (2.6). 7 2 (1.2) ω x (t), ω x +(t). lim t x +(t) x (t) =. x (t) x +(t). ω (1.2) 1 Lotka Volterra (1.2) dk ij (s). Lotka Volterra dx i (t) = x i (t) b i (t) dt a ij (t)x j (t) ] b ij (t)x j (t τ ij ), i = 1, 2,..., n. 7, ξ 1, ξ 2,..., ξ n ξ i a ii, j i i = 1, 2,..., n a ii ξ j a ij ξ j b ij > = inf t a ii (t), a ij = sup t a ij (t) (i j), b ij = sup t b ij (t). τ ij
36 31 (3.1) (3.2) t, 7 (3.4) (3.1) (3.2) 3 1 ξ 1, ξ 2,..., ξ n ξ i a ii (t) ξ j a ij (t) ξ j dk ij (s) >, j i t < ω i = 1, 2,..., n (1.2) ω Lotka Volterra (1) (2) (3) 31] Lotka Volterra (complete stability), () Lotka Volterra Lotka Volterra
37 1] 24. 2] T. Asai, M. Ohtani, and H. Yonezu. Analog integrated circuits for the Lotka Volterra competitive neural networks. IEEE Trans. Neural Networks, 1: , ] H. Bereketoglu and I. Győri. Global asymptotic stability in a nonautonomous Lotka Volterra type system with infinite delay. J. Math. Anal. Appl., 21: , ] A. Berman and R.J. Plemmons. Nonnegative matrices in the mathematical sciences. Academic, New York, ] F. Brauer and C. Castillo-Chávez. Mathematical Models in Population Biology and Epidemiology. Springer, New York, 21. 6] J.C. Eilbeck and J. López-Gómez. On the periodic Lotka Volterra competition model. J. Math. Anal. Appl., 21: 58 87, ] T. Fukai and S. Tanaka. A simple neural network exhibiting selective activation of neuronal ensembles: From winner-take-all to winners-share-all. Neural Comput., 9: 77 97, ] K. Gopalsamy. Stability and Oscillations in Delay Differential Equations of Population Dynamics. Kluwer Academic, Dordrecht, Netherlands, ] Y. Kuang. Delay Differential Equations with Applications in Population Dynamics. Academic, Boston, ] Y. Li. Periodic solutions for delay Lotka Volterra competition systems. J. Math. Anal. Appl., 246: , 2. 11] Y. Li and Y. Kuang. Periodic solutions of periodic delay Lotka Volterra equations and systems. J. Math. Anal. Appl., 255: 26 28, ] A.J. Lotka. Elements of Physical Biology. Williams & Wilkins, Baltimore, 1925.
38 33 13] W. Lu and T. Chen. On periodic dynamical systems. Chinese Ann. Math. Ser. B, 25: , ] O. Malcai, O. Biham, P. Richmond, and S. Solomon. Theoretical analysis and simulations of the generalized Lotka Volterra model. Phys. Rev. E, 66: 3112, ] F. Montes de Oca and M.L. Zeeman. Balancing Survival and extinction in nonautonomous competitive Lotka Volterra systems. J. Math. Anal. Appl., 192: 36 37, ] F. Montes de Oca and M.L. Zeeman. Extinction in nonautonomous competitive Lotka Volterra systems. Proc. Amer. Math. Soc., 124: , ] Y. Moreau, S. Louies, J. Vandewalle, and L. Brenig. Embedding recurrent neural networks into predator prey models. Neural Networks, 12: , ] S.A. Morris and D. Pratt. Analysis of the Lotka Volterra competition equations as a technological substitution model. Technol. Forecast. Soc. Change, 7: , ] J.D. Murray. Mathematical Biology I: An Introduction. Springer, New York, 3rd ed., 22. 2] J.M. Peña. A stable test to check if a matrix is a nonsingular M-matrix. Math. Comput., 73: , ] H.L. Smith. Monotone Dynamical Systems: An Introduction to the Theory of Competitive and Cooperative Systems. American Mathematical Society, Providence, ] J.C. Sprott. Competition with evolution in ecology and finance. Phys. Lett. A, 325: , ] Y. Takeuchi. Global Dynamical Properties of Lotka Volterra Systems. World Scientific, Singapore, ] B. Tang and Y. Kuang. Existence, uniqueness and asymptotic stability of periodic solutions of periodic functional differential systems. Tohoku Math. J., 49: , ] Z. Teng. Nonautonomous Lotka Volterra systems with delays. J. Differential Equations, 179: , 22.
39 34 26] Z. Teng and L. Chen. Global asymptotic stability of periodic Lotka Volterra systems with delays. Nonlinear Anal., 45: , ] Z. Teng and Y. Yu. Some new results of nonautonomous Lotka Volterra competitive systems with delays. J. Math. Anal. Appl., 241: , 2. 28] A. Tineo. Existence of global attractors for a class of periodic Kolmogorov systems. J. Math. Anal. Appl., 279: , ] V. Volterra. Variazionie fluttuazioni del numero d individui in specie animali conviventi. Mem. Acad. Lincei., 2: , Variations and fluctuations of the number of individuals in animal species living together. Translation by R.N. Chapman. In: Animal Ecology. pp McGraw Hill, New York, ] W. Wang, P. Fergola, and C. Tenneriello. Global attractivity of periodic solutions of population models. J. Math. Anal. Appl., 211: , ] Z. Yi and K.K. Tan. Dynamic stability conditions for Lotka Volterra recurrent neural networks with delays. Phys. Rev. E, 66: 1191, ] J. Zhou, Z.R. Liu, and G.R. Chen. Dynamics of periodic delayed neural networks. Neural Networks, 17: 87 11, 24.
40
41 1] W. Lin and T. Chen. Positive periodic solutions of delayed periodic Lotka Volterra systems. Phys. Lett. A, 334: , 25. 2] W. Lin and T. Chen. Controlling chaos in a chaotic neuron model. Int. J. Bifurcation Chaos, accepted for publication. 3] W. Lin and T. Chen. Analysis of two restart algorithms. Neurocomputing, accepted for publication. 4] W. Lin and T. Chen. Dynamics of delayed periodic Lotka Volterra systems.
J. of Math. (PRC) 6 n (nt ) + n V = 0, (1.1) n t + div. div(n T ) = n τ (T L(x) T ), (1.2) n)xx (nt ) x + nv x = J 0, (1.4) n. 6 n
Vol. 35 ( 215 ) No. 5 J. of Math. (PRC) a, b, a ( a. ; b., 4515) :., [3]. : ; ; MR(21) : 35Q4 : O175. : A : 255-7797(215)5-15-7 1 [1] : [ ( ) ] ε 2 n n t + div 6 n (nt ) + n V =, (1.1) n div(n T ) = n
Διαβάστε περισσότεραJ. of Math. (PRC) Banach, , X = N(T ) R(T + ), Y = R(T ) N(T + ). Vol. 37 ( 2017 ) No. 5
Vol. 37 ( 2017 ) No. 5 J. of Math. (PRC) 1,2, 1, 1 (1., 225002) (2., 225009) :. I +AT +, T + = T + (I +AT + ) 1, T +. Banach Hilbert Moore-Penrose.. : ; ; Moore-Penrose ; ; MR(2010) : 47L05; 46A32 : O177.2
Διαβάστε περισσότεραPrey-Taxis Holling-Tanner
Vol. 28 ( 2018 ) No. 1 J. of Math. (PRC) Prey-Taxis Holling-Tanner, (, 730070) : prey-taxis Holling-Tanner.,,.. : Holling-Tanner ; prey-taxis; ; MR(2010) : 35B32; 35B36 : O175.26 : A : 0255-7797(2018)01-0140-07
Διαβάστε περισσότεραSCITECH Volume 13, Issue 2 RESEARCH ORGANISATION Published online: March 29, 2018
Journal of rogressive Research in Mathematics(JRM) ISSN: 2395-028 SCITECH Volume 3, Issue 2 RESEARCH ORGANISATION ublished online: March 29, 208 Journal of rogressive Research in Mathematics www.scitecresearch.com/journals
Διαβάστε περισσότεραCHAPTER 25 SOLVING EQUATIONS BY ITERATIVE METHODS
CHAPTER 5 SOLVING EQUATIONS BY ITERATIVE METHODS EXERCISE 104 Page 8 1. Find the positive root of the equation x + 3x 5 = 0, correct to 3 significant figures, using the method of bisection. Let f(x) =
Διαβάστε περισσότεραD Alembert s Solution to the Wave Equation
D Alembert s Solution to the Wave Equation MATH 467 Partial Differential Equations J. Robert Buchanan Department of Mathematics Fall 2018 Objectives In this lesson we will learn: a change of variable technique
Διαβάστε περισσότεραPartial Differential Equations in Biology The boundary element method. March 26, 2013
The boundary element method March 26, 203 Introduction and notation The problem: u = f in D R d u = ϕ in Γ D u n = g on Γ N, where D = Γ D Γ N, Γ D Γ N = (possibly, Γ D = [Neumann problem] or Γ N = [Dirichlet
Διαβάστε περισσότεραX g 1990 g PSRB
e-mail: shibata@provence.c.u-tokyo.ac.jp 2005 1. 40 % 1 4 1) 1 PSRB1913 16 30 2) 3) X g 1990 g 4) g g 2 g 2. 1990 2000 3) 10 1 Page 1 5) % 1 g g 3. 1 3 1 6) 3 S S S n m (1/a, b k /a) a b k 1 1 3 S n m,
Διαβάστε περισσότεραOther Test Constructions: Likelihood Ratio & Bayes Tests
Other Test Constructions: Likelihood Ratio & Bayes Tests Side-Note: So far we have seen a few approaches for creating tests such as Neyman-Pearson Lemma ( most powerful tests of H 0 : θ = θ 0 vs H 1 :
Διαβάστε περισσότεραChapter 6: Systems of Linear Differential. be continuous functions on the interval
Chapter 6: Systems of Linear Differential Equations Let a (t), a 2 (t),..., a nn (t), b (t), b 2 (t),..., b n (t) be continuous functions on the interval I. The system of n first-order differential equations
Διαβάστε περισσότεραHomomorphism in Intuitionistic Fuzzy Automata
International Journal of Fuzzy Mathematics Systems. ISSN 2248-9940 Volume 3, Number 1 (2013), pp. 39-45 Research India Publications http://www.ripublication.com/ijfms.htm Homomorphism in Intuitionistic
Διαβάστε περισσότεραA Bonus-Malus System as a Markov Set-Chain. Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics
A Bonus-Malus System as a Markov Set-Chain Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics Contents 1. Markov set-chain 2. Model of bonus-malus system 3. Example 4. Conclusions
Διαβάστε περισσότεραMatrices and Determinants
Matrices and Determinants SUBJECTIVE PROBLEMS: Q 1. For what value of k do the following system of equations possess a non-trivial (i.e., not all zero) solution over the set of rationals Q? x + ky + 3z
Διαβάστε περισσότεραDiracDelta. Notations. Primary definition. Specific values. General characteristics. Traditional name. Traditional notation
DiracDelta Notations Traditional name Dirac delta function Traditional notation x Mathematica StandardForm notation DiracDeltax Primary definition 4.03.02.000.0 x Π lim ε ; x ε0 x 2 2 ε Specific values
Διαβάστε περισσότεραSOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr (T t N n) Pr (max (X 1,..., X N ) t N n) Pr (max
Διαβάστε περισσότεραFeasible Regions Defined by Stability Constraints Based on the Argument Principle
Feasible Regions Defined by Stability Constraints Based on the Argument Principle Ken KOUNO Masahide ABE Masayuki KAWAMATA Department of Electronic Engineering, Graduate School of Engineering, Tohoku University
Διαβάστε περισσότεραAreas and Lengths in Polar Coordinates
Kiryl Tsishchanka Areas and Lengths in Polar Coordinates In this section we develop the formula for the area of a region whose boundary is given by a polar equation. We need to use the formula for the
Διαβάστε περισσότεραExample Sheet 3 Solutions
Example Sheet 3 Solutions. i Regular Sturm-Liouville. ii Singular Sturm-Liouville mixed boundary conditions. iii Not Sturm-Liouville ODE is not in Sturm-Liouville form. iv Regular Sturm-Liouville note
Διαβάστε περισσότεραΜηχανισμοί πρόβλεψης προσήμων σε προσημασμένα μοντέλα κοινωνικών δικτύων ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ
ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ ΣΧΟΛΗ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΜΗΧΑΝΙΚΩΝ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ ΕΠΙΚΟΙΝΩΝΙΩΝ, ΗΛΕΚΤΡΟΝΙΚΗΣ ΚΑΙ ΣΥΣΤΗΜΑΤΩΝ ΠΛΗΡΟΦΟΡΙΚΗΣ Μηχανισμοί πρόβλεψης προσήμων σε προσημασμένα μοντέλα κοινωνικών
Διαβάστε περισσότεραΠΟΛΥΤΕΧΝΕΙΟ ΚΡΗΤΗΣ ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ ΠΕΡΙΒΑΛΛΟΝΤΟΣ
ΠΟΛΥΤΕΧΝΕΙΟ ΚΡΗΤΗΣ ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ ΠΕΡΙΒΑΛΛΟΝΤΟΣ Τομέας Περιβαλλοντικής Υδραυλικής και Γεωπεριβαλλοντικής Μηχανικής (III) Εργαστήριο Γεωπεριβαλλοντικής Μηχανικής TECHNICAL UNIVERSITY OF CRETE SCHOOL of
Διαβάστε περισσότεραExercises 10. Find a fundamental matrix of the given system of equations. Also find the fundamental matrix Φ(t) satisfying Φ(0) = I. 1.
Exercises 0 More exercises are available in Elementary Differential Equations. If you have a problem to solve any of them, feel free to come to office hour. Problem Find a fundamental matrix of the given
Διαβάστε περισσότεραAreas and Lengths in Polar Coordinates
Kiryl Tsishchanka Areas and Lengths in Polar Coordinates In this section we develop the formula for the area of a region whose boundary is given by a polar equation. We need to use the formula for the
Διαβάστε περισσότεραSOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr T t N n) Pr max X 1,..., X N ) t N n) Pr max
Διαβάστε περισσότερα: Monte Carlo EM 313, Louis (1982) EM, EM Newton-Raphson, /. EM, 2 Monte Carlo EM Newton-Raphson, Monte Carlo EM, Monte Carlo EM, /. 3, Monte Carlo EM
2008 6 Chinese Journal of Applied Probability and Statistics Vol.24 No.3 Jun. 2008 Monte Carlo EM 1,2 ( 1,, 200241; 2,, 310018) EM, E,,. Monte Carlo EM, EM E Monte Carlo,. EM, Monte Carlo EM,,,,. Newton-Raphson.
Διαβάστε περισσότεραInverse trigonometric functions & General Solution of Trigonometric Equations. ------------------ ----------------------------- -----------------
Inverse trigonometric functions & General Solution of Trigonometric Equations. 1. Sin ( ) = a) b) c) d) Ans b. Solution : Method 1. Ans a: 17 > 1 a) is rejected. w.k.t Sin ( sin ) = d is rejected. If sin
Διαβάστε περισσότερα([28] Bao-Feng Feng (UTP-TX), ( ), [20], [16], [24]. 1 ([3], [17]) p t = 1 2 κ2 T + κ s N -259-
5,..,. [8]..,,.,.., Bao-Feng Feng UTP-TX,, UTP-TX,,. [0], [6], [4].. ps ps, t. t ps, 0 = ps. s 970 [0] []. [3], [7] p t = κ T + κ s N -59- , κs, t κ t + 3 κ κ s + κ sss = 0. T s, t, Ns, t., - mkdv. mkdv.
Διαβάστε περισσότερα(, ) (SEM) [4] ,,,, , Legendre. [6] Gauss-Lobatto-Legendre (GLL) Legendre. Dubiner ,,,, (TSEM) Vol. 34 No. 4 Dec. 2017
34 4 17 1 JOURNAL OF SHANGHAI POLYTECHNIC UNIVERSITY Vol. 34 No. 4 Dec. 17 : 11-4543(174-83-8 DOI: 1.1957/j.cnki.jsspu.17.4.6 (, 19 :,,,,,, : ; ; ; ; ; : O 41.8 : A, [1],,,,, Jung [] Legendre, [3] Chebyshev
Διαβάστε περισσότεραGraded Refractive-Index
Graded Refractive-Index Common Devices Methodologies for Graded Refractive Index Methodologies: Ray Optics WKB Multilayer Modelling Solution requires: some knowledge of index profile n 2 x Ray Optics for
Διαβάστε περισσότεραOrdinal Arithmetic: Addition, Multiplication, Exponentiation and Limit
Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit Ting Zhang Stanford May 11, 2001 Stanford, 5/11/2001 1 Outline Ordinal Classification Ordinal Addition Ordinal Multiplication Ordinal
Διαβάστε περισσότεραCoefficient Inequalities for a New Subclass of K-uniformly Convex Functions
International Journal of Computational Science and Mathematics. ISSN 0974-89 Volume, Number (00), pp. 67--75 International Research Publication House http://www.irphouse.com Coefficient Inequalities for
Διαβάστε περισσότεραST5224: Advanced Statistical Theory II
ST5224: Advanced Statistical Theory II 2014/2015: Semester II Tutorial 7 1. Let X be a sample from a population P and consider testing hypotheses H 0 : P = P 0 versus H 1 : P = P 1, where P j is a known
Διαβάστε περισσότεραFourier Series. MATH 211, Calculus II. J. Robert Buchanan. Spring Department of Mathematics
Fourier Series MATH 211, Calculus II J. Robert Buchanan Department of Mathematics Spring 2018 Introduction Not all functions can be represented by Taylor series. f (k) (c) A Taylor series f (x) = (x c)
Διαβάστε περισσότεραSrednicki Chapter 55
Srednicki Chapter 55 QFT Problems & Solutions A. George August 3, 03 Srednicki 55.. Use equations 55.3-55.0 and A i, A j ] = Π i, Π j ] = 0 (at equal times) to verify equations 55.-55.3. This is our third
Διαβάστε περισσότεραPhys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required)
Phys460.nb 81 ψ n (t) is still the (same) eigenstate of H But for tdependent H. The answer is NO. 5.5.5. Solution for the tdependent Schrodinger s equation If we assume that at time t 0, the electron starts
Διαβάστε περισσότεραDynamics in BAM Fuzzy Neural Networks with Delays and Reaction-Diffusion Terms 1
International Mathematical Forum, 3, 2008, no. 20, 979-1000 Dynamics in BAM Fuzzy Neural Networks with Delays and Reaction-Diffusion Terms 1 Zuoan Li Department of Mathematics Sichuan University of Science
Διαβάστε περισσότεραMock Exam 7. 1 Hong Kong Educational Publishing Company. Section A 1. Reference: HKDSE Math M Q2 (a) (1 + kx) n 1M + 1A = (1) =
Mock Eam 7 Mock Eam 7 Section A. Reference: HKDSE Math M 0 Q (a) ( + k) n nn ( )( k) + nk ( ) + + nn ( ) k + nk + + + A nk... () nn ( ) k... () From (), k...() n Substituting () into (), nn ( ) n 76n 76n
Διαβάστε περισσότεραCHAPTER 101 FOURIER SERIES FOR PERIODIC FUNCTIONS OF PERIOD
CHAPTER FOURIER SERIES FOR PERIODIC FUNCTIONS OF PERIOD EXERCISE 36 Page 66. Determine the Fourier series for the periodic function: f(x), when x +, when x which is periodic outside this rge of period.
Διαβάστε περισσότεραSCHOOL OF MATHEMATICAL SCIENCES G11LMA Linear Mathematics Examination Solutions
SCHOOL OF MATHEMATICAL SCIENCES GLMA Linear Mathematics 00- Examination Solutions. (a) i. ( + 5i)( i) = (6 + 5) + (5 )i = + i. Real part is, imaginary part is. (b) ii. + 5i i ( + 5i)( + i) = ( i)( + i)
Διαβάστε περισσότεραΧρηματοοικονομική Ανάπτυξη, Θεσμοί και
ΑΡΙΣΤΟΤΕΛΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ ΣΧΟΛΗ ΝΟΜΙΚΩΝ, ΟΙΚΟΝΟΜΙΚΩΝ ΚΑΙ ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΤΜΗΜΑ ΟΙΚΟΝΟΜΙΚΩΝ ΕΠΙΣΤΗΜΩΝ Τομέας Ανάπτυξης και Προγραμματισμού Χρηματοοικονομική Ανάπτυξη, Θεσμοί και Οικονομική
Διαβάστε περισσότεραHigh order interpolation function for surface contact problem
3 016 5 Journal of East China Normal University Natural Science No 3 May 016 : 1000-564101603-0009-1 1 1 1 00444; E- 00030 : Lagrange Lobatto Matlab : ; Lagrange; : O41 : A DOI: 103969/jissn1000-56410160300
Διαβάστε περισσότερα2 Composition. Invertible Mappings
Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan Composition. Invertible Mappings In this section we discuss two procedures for creating new mappings from old ones, namely,
Διαβάστε περισσότεραHomework 8 Model Solution Section
MATH 004 Homework Solution Homework 8 Model Solution Section 14.5 14.6. 14.5. Use the Chain Rule to find dz where z cosx + 4y), x 5t 4, y 1 t. dz dx + dy y sinx + 4y)0t + 4) sinx + 4y) 1t ) 0t + 4t ) sinx
Διαβάστε περισσότερα«Χρήσεις γης, αξίες γης και κυκλοφοριακές ρυθμίσεις στο Δήμο Χαλκιδέων. Η μεταξύ τους σχέση και εξέλιξη.»
ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ ΣΧΟΛΗ ΑΓΡΟΝΟΜΩΝ ΚΑΙ ΤΟΠΟΓΡΑΦΩΝ ΜΗΧΑΝΙΚΩΝ ΤΟΜΕΑΣ ΓΕΩΓΡΑΦΙΑΣ ΚΑΙ ΠΕΡΙΦΕΡΕΙΑΚΟΥ ΣΧΕΔΙΑΣΜΟΥ ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ: «Χρήσεις γης, αξίες γης και κυκλοφοριακές ρυθμίσεις στο Δήμο Χαλκιδέων.
Διαβάστε περισσότεραSection 8.3 Trigonometric Equations
99 Section 8. Trigonometric Equations Objective 1: Solve Equations Involving One Trigonometric Function. In this section and the next, we will exple how to solving equations involving trigonometric functions.
Διαβάστε περισσότεραSecond Order Partial Differential Equations
Chapter 7 Second Order Partial Differential Equations 7.1 Introduction A second order linear PDE in two independent variables (x, y Ω can be written as A(x, y u x + B(x, y u xy + C(x, y u u u + D(x, y
Διαβάστε περισσότεραChapter 6: Systems of Linear Differential. be continuous functions on the interval
Chapter 6: Systems of Linear Differential Equations Let a (t), a 2 (t),..., a nn (t), b (t), b 2 (t),..., b n (t) be continuous functions on the interval I. The system of n first-order differential equations
Διαβάστε περισσότεραThe behavior of solutions of second order delay differential equations
J. Math. Anal. Appl. 332 27) 1278 129 www.elsevier.com/locate/jmaa The behavior of solutions of second order delay differential equations Ali Fuat Yeniçerioğlu Department of Mathematics, The Faculty of
Διαβάστε περισσότεραΗλεκτρονικοί Υπολογιστές IV
ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΜΑΘΗΜΑΤΑ Ηλεκτρονικοί Υπολογιστές IV Εισαγωγή στα δυναμικά συστήματα Διδάσκων: Επίκουρος Καθηγητής Αθανάσιος Σταυρακούδης Άδειες Χρήσης Το παρόν εκπαιδευτικό υλικό
Διαβάστε περισσότεραANSWERSHEET (TOPIC = DIFFERENTIAL CALCULUS) COLLECTION #2. h 0 h h 0 h h 0 ( ) g k = g 0 + g 1 + g g 2009 =?
Teko Classes IITJEE/AIEEE Maths by SUHAAG SIR, Bhopal, Ph (0755) 3 00 000 www.tekoclasses.com ANSWERSHEET (TOPIC DIFFERENTIAL CALCULUS) COLLECTION # Question Type A.Single Correct Type Q. (A) Sol least
Διαβάστε περισσότεραΓΕΩΠΟΝΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ ΤΜΗΜΑ ΑΓΡΟΤΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ & ΑΝΑΠΤΥΞΗΣ
ΓΕΩΠΟΝΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ ΤΜΗΜΑ ΑΓΡΟΤΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ & ΑΝΑΠΤΥΞΗΣ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ «ΟΛΟΚΛΗΡΩΜΕΝΗ ΑΝΑΠΤΥΞΗ & ΔΙΑΧΕΙΡΙΣΗ ΤΟΥ ΑΓΡΟΤΙΚΟΥ ΧΩΡΟΥ» ΜΕΤΑΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ «Οικονομετρική διερεύνηση
Διαβάστε περισσότερα«Μοντελοποίηση και Αριθµητικές Προσοµοιώσεις» Εισαγωγή στη Μαθηµατική Βιολογία. Πληθυσµιακά Μοντέλα
«Μοντελοποίηση και Αριθµητικές Προσοµοιώσεις» Εισαγωγή στη Μαθηµατική Βιολογία Μοντέλα Πληθυσµών Ενός Είδους: Συνεχή Διακριτά Μοντέλα Αλληλεπιδρώντων Πληθυσµών: Συνεχή Διακριτά Μαθηµατική Μοντελοποίηση:
Διαβάστε περισσότεραΠΕΡΙΕΧΟΜΕΝΑ. Μάρκετινγκ Αθλητικών Τουριστικών Προορισμών 1
ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΙΓΑΙΟΥ ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΤΗΣ ΔΙΟΙΚΗΣΗΣ ΤΜΗΜΑ ΔΙΟΙΚΗΣΗΣ ΕΠΙΧΕΙΡΗΣΕΩΝ ΔΙΑΤΜΗΜΑΤΙΚΟ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ «Σχεδιασμός, Διοίκηση και Πολιτική του Τουρισμού» ΜΑΡΚΕΤΙΝΓΚ ΑΘΛΗΤΙΚΩΝ ΤΟΥΡΙΣΤΙΚΩΝ
Διαβάστε περισσότεραSPECIAL FUNCTIONS and POLYNOMIALS
SPECIAL FUNCTIONS and POLYNOMIALS Gerard t Hooft Stefan Nobbenhuis Institute for Theoretical Physics Utrecht University, Leuvenlaan 4 3584 CC Utrecht, the Netherlands and Spinoza Institute Postbox 8.195
Διαβάστε περισσότεραJ. of Math. (PRC) u(t k ) = I k (u(t k )), k = 1, 2,, (1.6) , [3, 4] (1.1), (1.2), (1.3), [6 8]
Vol 36 ( 216 ) No 3 J of Mah (PR) 1, 2, 3 (1, 4335) (2, 4365) (3, 431) :,,,, : ; ; ; MR(21) : 35A1; 35A2 : O17529 : A : 255-7797(216)3-591-7 1 d d [x() g(, x )] = f(, x ),, (11) x = ϕ(), [ r, ], (12) x(
Διαβάστε περισσότεραA General Note on δ-quasi Monotone and Increasing Sequence
International Mathematical Forum, 4, 2009, no. 3, 143-149 A General Note on δ-quasi Monotone and Increasing Sequence Santosh Kr. Saxena H. N. 419, Jawaharpuri, Badaun, U.P., India Presently working in
Διαβάστε περισσότεραThe Negative Neumann Eigenvalues of Second Order Differential Equation with Two Turning Points
Applied Mathematical Sciences, Vol. 3, 009, no., 6-66 The Negative Neumann Eigenvalues of Second Order Differential Equation with Two Turning Points A. Neamaty and E. A. Sazgar Department of Mathematics,
Διαβάστε περισσότεραUniform Convergence of Fourier Series Michael Taylor
Uniform Convergence of Fourier Series Michael Taylor Given f L 1 T 1 ), we consider the partial sums of the Fourier series of f: N 1) S N fθ) = ˆfk)e ikθ. k= N A calculation gives the Dirichlet formula
Διαβάστε περισσότεραStatistical Inference I Locally most powerful tests
Statistical Inference I Locally most powerful tests Shirsendu Mukherjee Department of Statistics, Asutosh College, Kolkata, India. shirsendu st@yahoo.co.in So far we have treated the testing of one-sided
Διαβάστε περισσότεραΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ ΕΠΑΝΑΣΧΕΔΙΑΣΜΟΣ ΓΡΑΜΜΗΣ ΣΥΝΑΡΜΟΛΟΓΗΣΗΣ ΜΕ ΧΡΗΣΗ ΕΡΓΑΛΕΙΩΝ ΛΙΤΗΣ ΠΑΡΑΓΩΓΗΣ REDESIGNING AN ASSEMBLY LINE WITH LEAN PRODUCTION TOOLS
ΔΙΑΤΜΗΜΑΤΙΚΟ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ ΣΤΗ ΔΙΟΙΚΗΣΗ ΤΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ ΕΠΑΝΑΣΧΕΔΙΑΣΜΟΣ ΓΡΑΜΜΗΣ ΣΥΝΑΡΜΟΛΟΓΗΣΗΣ ΜΕ ΧΡΗΣΗ ΕΡΓΑΛΕΙΩΝ ΛΙΤΗΣ ΠΑΡΑΓΩΓΗΣ REDESIGNING AN ASSEMBLY LINE WITH
Διαβάστε περισσότεραSOME PROPERTIES OF FUZZY REAL NUMBERS
Sahand Communications in Mathematical Analysis (SCMA) Vol. 3 No. 1 (2016), 21-27 http://scma.maragheh.ac.ir SOME PROPERTIES OF FUZZY REAL NUMBERS BAYAZ DARABY 1 AND JAVAD JAFARI 2 Abstract. In the mathematical
Διαβάστε περισσότερα6.1. Dirac Equation. Hamiltonian. Dirac Eq.
6.1. Dirac Equation Ref: M.Kaku, Quantum Field Theory, Oxford Univ Press (1993) η μν = η μν = diag(1, -1, -1, -1) p 0 = p 0 p = p i = -p i p μ p μ = p 0 p 0 + p i p i = E c 2 - p 2 = (m c) 2 H = c p 2
Διαβάστε περισσότεραΗ ΠΡΟΣΩΠΙΚΗ ΟΡΙΟΘΕΤΗΣΗ ΤΟΥ ΧΩΡΟΥ Η ΠΕΡΙΠΤΩΣΗ ΤΩΝ CHAT ROOMS
ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΤΕΧΝΟΛΟΓΙΚΟ ΕΚΠΑΙΔΕΥΤΙΚΟ ΙΔΡΥΜΑ Ι Ο Ν Ι Ω Ν Ν Η Σ Ω Ν ΤΜΗΜΑ ΔΗΜΟΣΙΩΝ ΣΧΕΣΕΩΝ & ΕΠΙΚΟΙΝΩΝΙΑΣ Ταχ. Δ/νση : ΑΤΕΙ Ιονίων Νήσων- Λεωφόρος Αντώνη Τρίτση Αργοστόλι Κεφαλληνίας, Ελλάδα 28100,+30
Διαβάστε περισσότεραTridiagonal matrices. Gérard MEURANT. October, 2008
Tridiagonal matrices Gérard MEURANT October, 2008 1 Similarity 2 Cholesy factorizations 3 Eigenvalues 4 Inverse Similarity Let α 1 ω 1 β 1 α 2 ω 2 T =......... β 2 α 1 ω 1 β 1 α and β i ω i, i = 1,...,
Διαβάστε περισσότεραΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ ΤΜΗΜΑ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ ΣΥΣΤΗΜΑΤΩΝ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ
ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ ΤΜΗΜΑ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ ΣΥΣΤΗΜΑΤΩΝ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ ιπλωµατική Εργασία του φοιτητή του τµήµατος Ηλεκτρολόγων Μηχανικών και Τεχνολογίας Ηλεκτρονικών
Διαβάστε περισσότεραNumerical Analysis FMN011
Numerical Analysis FMN011 Carmen Arévalo Lund University carmen@maths.lth.se Lecture 12 Periodic data A function g has period P if g(x + P ) = g(x) Model: Trigonometric polynomial of order M T M (x) =
Διαβάστε περισσότεραHomework 3 Solutions
Homework 3 Solutions Igor Yanovsky (Math 151A TA) Problem 1: Compute the absolute error and relative error in approximations of p by p. (Use calculator!) a) p π, p 22/7; b) p π, p 3.141. Solution: For
Διαβάστε περισσότεραThe Simply Typed Lambda Calculus
Type Inference Instead of writing type annotations, can we use an algorithm to infer what the type annotations should be? That depends on the type system. For simple type systems the answer is yes, and
Διαβάστε περισσότεραΠΑΝΔΠΗΣΖΜΗΟ ΠΑΣΡΩΝ ΓΗΑΣΜΖΜΑΣΗΚΟ ΠΡΟΓΡΑΜΜΑ ΜΔΣΑΠΣΤΥΗΑΚΩΝ ΠΟΤΓΩΝ «ΤΣΖΜΑΣΑ ΔΠΔΞΔΡΓΑΗΑ ΖΜΑΣΩΝ ΚΑΗ ΔΠΗΚΟΗΝΩΝΗΩΝ» ΣΜΖΜΑ ΜΖΥΑΝΗΚΩΝ Ζ/Τ ΚΑΗ ΠΛΖΡΟΦΟΡΗΚΖ
ΠΑΝΔΠΗΣΖΜΗΟ ΠΑΣΡΩΝ ΓΗΑΣΜΖΜΑΣΗΚΟ ΠΡΟΓΡΑΜΜΑ ΜΔΣΑΠΣΤΥΗΑΚΩΝ ΠΟΤΓΩΝ «ΤΣΖΜΑΣΑ ΔΠΔΞΔΡΓΑΗΑ ΖΜΑΣΩΝ ΚΑΗ ΔΠΗΚΟΗΝΩΝΗΩΝ» ΣΜΖΜΑ ΜΖΥΑΝΗΚΩΝ Ζ/Τ ΚΑΗ ΠΛΖΡΟΦΟΡΗΚΖ ΣΜΖΜΑ ΖΛΔΚΣΡΟΛΟΓΩΝ ΜΖΥΑΝΗΚΩΝ ΚΑΗ ΣΔΥΝΟΛΟΓΗΑ ΤΠΟΛΟΓΗΣΩΝ ΣΜΖΜΑ
Διαβάστε περισσότερα6.3 Forecasting ARMA processes
122 CHAPTER 6. ARMA MODELS 6.3 Forecasting ARMA processes The purpose of forecasting is to predict future values of a TS based on the data collected to the present. In this section we will discuss a linear
Διαβάστε περισσότεραReminders: linear functions
Reminders: linear functions Let U and V be vector spaces over the same field F. Definition A function f : U V is linear if for every u 1, u 2 U, f (u 1 + u 2 ) = f (u 1 ) + f (u 2 ), and for every u U
Διαβάστε περισσότεραLifting Entry (continued)
ifting Entry (continued) Basic planar dynamics of motion, again Yet another equilibrium glide Hypersonic phugoid motion Planar state equations MARYAN 1 01 avid. Akin - All rights reserved http://spacecraft.ssl.umd.edu
Διαβάστε περισσότεραLecture 2: Dirac notation and a review of linear algebra Read Sakurai chapter 1, Baym chatper 3
Lecture 2: Dirac notation and a review of linear algebra Read Sakurai chapter 1, Baym chatper 3 1 State vector space and the dual space Space of wavefunctions The space of wavefunctions is the set of all
Διαβάστε περισσότεραA Two-Sided Laplace Inversion Algorithm with Computable Error Bounds and Its Applications in Financial Engineering
Electronic Companion A Two-Sie Laplace Inversion Algorithm with Computable Error Bouns an Its Applications in Financial Engineering Ning Cai, S. G. Kou, Zongjian Liu HKUST an Columbia University Appenix
Διαβάστε περισσότεραΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ ΤΜΗΜΑ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ ΣΥΣΤΗΜΑΤΩΝ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ
ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ ΤΜΗΜΑ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ ΣΥΣΤΗΜΑΤΩΝ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ Διπλωματική Εργασία του φοιτητή του τμήματος Ηλεκτρολόγων Μηχανικών και Τεχνολογίας Ηλεκτρονικών
Διαβάστε περισσότερα3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β
3.4 SUM AND DIFFERENCE FORMULAS Page Theorem cos(αβ cos α cos β -sin α cos(α-β cos α cos β sin α NOTE: cos(αβ cos α cos β cos(α-β cos α -cos β Proof of cos(α-β cos α cos β sin α Let s use a unit circle
Διαβάστε περισσότεραCongruence Classes of Invertible Matrices of Order 3 over F 2
International Journal of Algebra, Vol. 8, 24, no. 5, 239-246 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/.2988/ija.24.422 Congruence Classes of Invertible Matrices of Order 3 over F 2 Ligong An and
Διαβάστε περισσότεραΑΝΩΤΑΤΟ ΤΕΧΝΟΛΟΓΙΚΟ ΕΚΠΑΙΔΕΥΤΙΚΟ ΙΔΡΥΜΑ ΠΕΙΡΑΙΑ ΣΧΟΛΗ ΤΕΧΝΟΛΟΓΙΚΩΝ ΕΦΑΡΜΟΓΩΝ ΤΜΗΜΑ ΠΟΛΙΤΙΚΩΝ ΔΟΜΙΚΩΝ ΕΡΓΩΝ
ΑΝΩΤΑΤΟ ΤΕΧΝΟΛΟΓΙΚΟ ΕΚΠΑΙΔΕΥΤΙΚΟ ΙΔΡΥΜΑ ΠΕΙΡΑΙΑ ΣΧΟΛΗ ΤΕΧΝΟΛΟΓΙΚΩΝ ΕΦΑΡΜΟΓΩΝ ΤΜΗΜΑ ΠΟΛΙΤΙΚΩΝ ΔΟΜΙΚΩΝ ΕΡΓΩΝ Δυναμική Ανάλυση Κατασκευών με Κατανεμημένη Μάζα και Ακαμψία Πτυχιακή Εργασία Φουκάκη Βαρβάρα
Διαβάστε περισσότεραEvery set of first-order formulas is equivalent to an independent set
Every set of first-order formulas is equivalent to an independent set May 6, 2008 Abstract A set of first-order formulas, whatever the cardinality of the set of symbols, is equivalent to an independent
Διαβάστε περισσότεραu = g(u) in R N, u > 0 in R N, u H 1 (R N ).. (1), u 2 dx G(u) dx : H 1 (R N ) R
2017 : msjmeeting-2017sep-05i002 ( ) 1.. u = g(u) in R N, u > 0 in R N, u H 1 (R N ). (1), N 2, g C 1 g(0) = 0. g(s) = s + s p. (1), [8, 9, 17],., [15] g. (1), E(u) := 1 u 2 dx G(u) dx : H 1 (R N ) R 2
Διαβάστε περισσότεραSingle-value extension property for anti-diagonal operator matrices and their square
1 215 1 Journal of East China Normal University Natural Science No. 1 Jan. 215 : 1-56412151-95-8,, 71119 :, Hilbert. : ; ; : O177.2 : A DOI: 1.3969/j.issn.1-5641.215.1.11 Single-value extension property
Διαβάστε περισσότεραΣΤΑΤΙΚΗ ΜΗ ΓΡΑΜΜΙΚΗ ΑΝΑΛΥΣΗ ΚΑΛΩ ΙΩΤΩΝ ΚΑΤΑΣΚΕΥΩΝ
1 ΕΘΝΙΚΟ ΜΕΤΣΟΒΟ ΠΟΛΥΤΕΧΝΕΙΟ Σχολή Πολιτικών Μηχανικών ΠΜΣ οµοστατικός Σχεδιασµός και Ανάλυση Κατασκευών Εργαστήριο Μεταλλικών Κατασκευών Μεταπτυχιακή ιπλωµατική Εργασία ΣΤΑΤΙΚΗ ΜΗ ΓΡΑΜΜΙΚΗ ΑΝΑΛΥΣΗ ΚΑΛΩ
Διαβάστε περισσότεραCHAPTER 48 APPLICATIONS OF MATRICES AND DETERMINANTS
CHAPTER 48 APPLICATIONS OF MATRICES AND DETERMINANTS EXERCISE 01 Page 545 1. Use matrices to solve: 3x + 4y x + 5y + 7 3x + 4y x + 5y 7 Hence, 3 4 x 0 5 y 7 The inverse of 3 4 5 is: 1 5 4 1 5 4 15 8 3
Διαβάστε περισσότεραGeodesic Equations for the Wormhole Metric
Geodesic Equations for the Wormhole Metric Dr R Herman Physics & Physical Oceanography, UNCW February 14, 2018 The Wormhole Metric Morris and Thorne wormhole metric: [M S Morris, K S Thorne, Wormholes
Διαβάστε περισσότεραFigure A.2: MPC and MPCP Age Profiles (estimating ρ, ρ = 2, φ = 0.03)..
Supplemental Material (not for publication) Persistent vs. Permanent Income Shocks in the Buffer-Stock Model Jeppe Druedahl Thomas H. Jørgensen May, A Additional Figures and Tables Figure A.: Wealth and
Διαβάστε περισσότεραThe Pohozaev identity for the fractional Laplacian
The Pohozaev identity for the fractional Laplacian Xavier Ros-Oton Departament Matemàtica Aplicada I, Universitat Politècnica de Catalunya (joint work with Joaquim Serra) Xavier Ros-Oton (UPC) The Pohozaev
Διαβάστε περισσότεραΤΕΧΝΟΛΟΓΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΥΠΡΟΥ ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ
ΤΕΧΝΟΛΟΓΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΥΠΡΟΥ ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ Πτυχιακή Εργασία "Η ΣΗΜΑΝΤΙΚΟΤΗΤΑ ΤΟΥ ΜΗΤΡΙΚΟΥ ΘΗΛΑΣΜΟΥ ΣΤΗ ΠΡΟΛΗΨΗ ΤΗΣ ΠΑΙΔΙΚΗΣ ΠΑΧΥΣΑΡΚΙΑΣ" Ειρήνη Σωτηρίου Λεμεσός 2014 ΤΕΧΝΟΛΟΓΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ
Διαβάστε περισσότεραMatrices and vectors. Matrix and vector. a 11 a 12 a 1n a 21 a 22 a 2n A = b 1 b 2. b m. R m n, b = = ( a ij. a m1 a m2 a mn. def
Matrices and vectors Matrix and vector a 11 a 12 a 1n a 21 a 22 a 2n A = a m1 a m2 a mn def = ( a ij ) R m n, b = b 1 b 2 b m Rm Matrix and vectors in linear equations: example E 1 : x 1 + x 2 + 3x 4 =
Διαβάστε περισσότεραES440/ES911: CFD. Chapter 5. Solution of Linear Equation Systems
ES440/ES911: CFD Chapter 5. Solution of Linear Equation Systems Dr Yongmann M. Chung http://www.eng.warwick.ac.uk/staff/ymc/es440.html Y.M.Chung@warwick.ac.uk School of Engineering & Centre for Scientific
Διαβάστε περισσότεραForced Pendulum Numerical approach
Numerical approach UiO April 8, 2014 Physical problem and equation We have a pendulum of length l, with mass m. The pendulum is subject to gravitation as well as both a forcing and linear resistance force.
Διαβάστε περισσότεραGlobal nonlinear stability of steady solutions of the 3-D incompressible Euler equations with helical symmetry and with no swirl
Around Vortices: from Cont. to Quantum Mech. Global nonlinear stability of steady solutions of the 3-D incompressible Euler equations with helical symmetry and with no swirl Maicon José Benvenutti (UNICAMP)
Διαβάστε περισσότεραFinite Field Problems: Solutions
Finite Field Problems: Solutions 1. Let f = x 2 +1 Z 11 [x] and let F = Z 11 [x]/(f), a field. Let Solution: F =11 2 = 121, so F = 121 1 = 120. The possible orders are the divisors of 120. Solution: The
Διαβάστε περισσότεραSchedulability Analysis Algorithm for Timing Constraint Workflow Models
CIMS Vol.8No.72002pp.527-532 ( 100084) Petri Petri F270.7 A Schedulability Analysis Algorithm for Timing Constraint Workflow Models Li Huifang and Fan Yushun (Department of Automation, Tsinghua University,
Διαβάστε περισσότεραΑπόκριση σε Μοναδιαία Ωστική Δύναμη (Unit Impulse) Απόκριση σε Δυνάμεις Αυθαίρετα Μεταβαλλόμενες με το Χρόνο. Απόστολος Σ.
Απόκριση σε Δυνάμεις Αυθαίρετα Μεταβαλλόμενες με το Χρόνο The time integral of a force is referred to as impulse, is determined by and is obtained from: Newton s 2 nd Law of motion states that the action
Διαβάστε περισσότεραΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ
ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ ΤΜΗΜΑ ΠΟΛΙΤΙΚΩΝ ΜΗΧΑΝΙΚΩΝ ΤΟΜΕΑΣ ΟΜΟΣΤΑΤΙΚΗΣ ΕΡΓΑΣΤΗΡΙΟ ΜΕΤΑΛΛΙΚΩΝ ΚΑΤΑΣΚΕΥΩΝ ΕΙΣΑΓΩΓΗ ΣΤΟΝ ΑΥΤΟΜΑΤΟ ΕΛΕΓΧΟ ΤΩΝ ΚΑΤΑΣΚΕΥΩΝ Ανεµόµετρο AMD 1 Αισθητήρας AMD 2 11 ος όροφος Υπολογιστής
Διαβάστε περισσότεραECE 308 SIGNALS AND SYSTEMS FALL 2017 Answers to selected problems on prior years examinations
ECE 308 SIGNALS AND SYSTEMS FALL 07 Answers to selected problems on prior years examinations Answers to problems on Midterm Examination #, Spring 009. x(t) = r(t + ) r(t ) u(t ) r(t ) + r(t 3) + u(t +
Διαβάστε περισσότεραEE512: Error Control Coding
EE512: Error Control Coding Solution for Assignment on Finite Fields February 16, 2007 1. (a) Addition and Multiplication tables for GF (5) and GF (7) are shown in Tables 1 and 2. + 0 1 2 3 4 0 0 1 2 3
Διαβάστε περισσότεραΕΜΠΕΙΡΙΚΗ ΠΡΟΣΕΓΓΙΣΗ ΤΗΣ NASH ΙΣΟΡΡΟΠΙΑΣ
ΕΜΠΕΙΡΙΚΗ ΠΡΟΣΕΓΓΙΣΗ ΤΗΣ NASH ΙΣΟΡΡΟΠΙΑΣ ΒΛΑΧΟΠΟΥΛΟΥ ΑΘΑΝΑΣΙΑ (Α.Μ. 11/08) ΜΕΤΑΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ Επιβλέπων καθηγητής: Παπαναστασίου Ιωάννης Εξεταστές : Νούλας Αθανάσιος Ζαπράνης Αχιλλέας ιατµηµατικό Πρόγραµµα
Διαβάστε περισσότεραb. Use the parametrization from (a) to compute the area of S a as S a ds. Be sure to substitute for ds!
MTH U341 urface Integrals, tokes theorem, the divergence theorem To be turned in Wed., Dec. 1. 1. Let be the sphere of radius a, x 2 + y 2 + z 2 a 2. a. Use spherical coordinates (with ρ a) to parametrize.
Διαβάστε περισσότεραΔΙΕΡΕΥΝΗΣΗ ΤΗΣ ΣΕΞΟΥΑΛΙΚΗΣ ΔΡΑΣΤΗΡΙΟΤΗΤΑΣ ΤΩΝ ΓΥΝΑΙΚΩΝ ΚΑΤΑ ΤΗ ΔΙΑΡΚΕΙΑ ΤΗΣ ΕΓΚΥΜΟΣΥΝΗΣ ΤΕΧΝΟΛΟΓΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΥΠΡΟΥ ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ
ΤΕΧΝΟΛΟΓΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΥΠΡΟΥ ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ Πτυχιακή Εργασία ΔΙΕΡΕΥΝΗΣΗ ΤΗΣ ΣΕΞΟΥΑΛΙΚΗΣ ΔΡΑΣΤΗΡΙΟΤΗΤΑΣ ΤΩΝ ΓΥΝΑΙΚΩΝ ΚΑΤΑ ΤΗ ΔΙΑΡΚΕΙΑ ΤΗΣ ΕΓΚΥΜΟΣΥΝΗΣ ΑΝΔΡΕΟΥ ΣΤΕΦΑΝΙΑ Λεμεσός 2012 i ii ΤΕΧΝΟΛΟΓΙΚΟ
Διαβάστε περισσότεραEstimation of stability region for a class of switched linear systems with multiple equilibrium points
29 4 2012 4 1000 8152(2012)04 0409 06 Control Theory & Applications Vol 29 No 4 Apr 2012 12 1 (1 250061; 2 250353) ; ; ; TP273 A Estimation of stability region for a class of switched linear systems with
Διαβάστε περισσότερα