Nov Journal of Zhengzhou University Engineering Science Vol. 36 No FCM. A doi /j. issn

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Ἀριστόβουλος Παπαφιλίππου
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1 Nov Journal of Zhengzhou University Engineering Science Vol 36 No C C FCM FCM MIA MDC MDC MIA I FCM c FCM m FCM C TP18 A doi /j issn MIA MDC 1 FCM m FCM 1 1 FCM FCM HCM FCM X = x 1 x 2 x n c U = u c m FCM U u j 2 n i i = 1 2 c u K-means 4 5 C FCM 6 j c u GMM 7 i i = 1 SOM 8 SVM 9 i j u ELM 10 FCM i u > 0 FCM FCM FCM zzu edu cn

2 6 C 57 U MIA MDC C min J m U C = c u m d 2 x j c i 2 i = 1 d x j c i = x j - c i 3 MIA MDC n c d c C k k nt k 2 n k k c i = u = c k = 1 u m x j 4 u m { } 2 /-1-1 d ( d ) kj FCM FCM C U n k 1 step1 c m d CT k C k = n k d 2 CT k nt n k 9 槡 n = 1 ε k = 0 MDC U 0 C 0 d k step2 6 U k i jd k > 0 u k = c = 0u k { ( ) } r = 1 d k 2 /-1-1 d k rj = 1 j k u k = 0 step3 7 C k +1 k +1 ci = u k m x j 6 7 u k m m step4 C k k +1 - C < ε FCM k = k + 1 step2 m m 1 3 FCM m FCM 1FCM c m 2FCM FCM m m FCM m m m 1 + FCM m 2 Pal 2 FCM 2 1 c c I c FCM CT k k k = 1 2 c MIA MIA c = FCM FCM m 1 槡 c c k = 1 d 2 CT k 8 C k MDC c = mean d CT k CT k 10 ean MDC MIA I I c = MDC c /MIA c 11 MIA c MDC c I c I c c m m FCM

3 H m U C FCM I c - 1 > I c c = c + J m U C m 1 step2 H m U C u H m U C = - 1 n c u ln u n i = 1 = 0 u ln u 12 = 0 H m U C m min J m U * C * = arg { 13 s t H m U * C * min J m U C H m U C ( ) μ G = exp - α Jm U * C * max J m U * C * μ C = 1 + β 1 H m U * C * ( ) max H m U * C * α > 1 α = 1 5 β β = = argaxin μ G μ C 16 Fig 1 J m U C H m U C FCM FCM m I c FCM 96 1 FCM step1 c = 2 I 0 = 0 step2 m = 1 1 J m U * C * H m U * C * step4 m m < 2 5m = m step3 step5 12 c step6 1 2 FCM c I c c step7 2 < c < n I c - 1 > I c FCM The flow chart of adaptive FCM algorithm 96 step3 1 2 FCM c i x i = x i1 x i2 x i96 x' = x max x i1 x i2 x i

4 6 C 59 max x i1 x i2 x i96 i 8 FCM x i 3 2 FCM c FCM m 98 FCM 2 FCM FCM 98 c = 8 c = FCM MDC c MIA c I c I c c mfcm J m U * C * H m U * C * J m U C H m U C m FCM 1 M FCM J Fig 2 The classification result of 3 D adaptive FCM algorithm k - means 2 J FCM J FCM c 6 C J m FCM 7 EM J FCM c FCM 8 SOM C - J 8 SOM FCM SOM 9 FCM FCM D NAGI J YAP K S TIONG S K et al Nontechnical loss

5 detection for metered customers in power utility using support vector machines J IEEE Transactions on Power Delivery CHICCO G NAPOLI R PETAL P Customer characterization options for improving the tariff offer J IEEE Trans on Power Systems BEZDEK J C Pattern recognition with fuzzy objective function algorithms M NewYork Plenum Press Power Load Characteristic Classification Technology Research Based on an Optimal Fuzzy C-means Clustering Algorithm ZHAO Guosheng 1 NIU Zhenzhen 1 LIU Yongguang 2 SUN Chaoliang 2 1 School of Electrical Engineering Zhengzhou University Zhengzhou China 2 Henan Xu Ji Instrument Co Ltd Xuchang China Abstract In view of the disadvantages of the traditional Fuzzy C-means clustering algorithm the author proposes an adaptive FCM algorithm This algorithm is based on two clustering results evaluation index of within the class distance MIA and between the class distance MDC The ratio of MDC and MIA defined as I is an adaptive function to determine the clustering number c of FCM algorithm At the same time according to the fuzzy decision method we use the objective function and partition entropy of FCM algorithm together to determine the value of optimal fuzzy weighted m This algorithm not only overcomes the FCM algorithm disadvantage of not being able to determine the clustering number automatically and fuzzy weighted index needs to be given by experience but also the clustering result is optimal Finally the correctness and effectiveness of the algorithm were proved through example analysis Key words load clustering FCM load characteristic daily load curve 55 Study on Optimal Allocation Algorithm of the Visible Light Communication LED Array YE Huiying WANG Li LIU Jinliang School of Information Engineering Zhengzhou University Zhengzhou China Abstract In view of the limitation of the visible light communication light source layout optimization this paper presents a universal optimization layout of light source The simulation results show that using the light source layout of the algorithm not only can make the average area of the room reaches the maximum spectral efficiency and uniform distribution of illumination Illuminance level is between 400 lx and 1500 lx which can meet the demand for lighting Therefore this algorithm can be used as an optimization algorithm for the layout of the universal light source Key words visible light communication LED array layout optimization algorithm universal

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