Abstract：The Fuzzy C-Means clustering (FCM) algorithm has the defects of being sensitive to initial clustering center,being susceptible to noise point interference and poor robustness in solving the problem of speech underdetermined mixing matrix estimation. An improved WEighted FCM algorithm based on evolutionary programming (WE-FCM) was proposed to eliminate the defects. Firstly, the powerful search ability of Evolutionary Programming (EP) algorithm was used to optimize FCM for obtaining FCM algorithm based on EP (EP-FCM), in order to obtain a better initial clustering center. Then, the Local Outlier Factor (LOF) algorithm was used to perform weighting to reduce the effects of noise points. The simulation experiment results show that, the normalized mean square error value and the deviation angle value of the proposed algorithm were both much smaller than those of the classical K-means clustering, K-Hough, FCM algorithm based on Genetic Algorithm (GAFCM) and FCM algorithm based on Find Density Peaks (FDP-FCM) when the number of source signals were 3 and 4. The experimental results show that, the proposed algorithm significantly improves the robustness of FCM algorithm and the accuracy of mixing matrix estimation.
1 余先川,胡丹.盲源分离理论与应用[M].北京:科学出版社, 2011:3-9. YUX C, HUD. Theory and Application of Blind Source Separation [M]. Beijing: Science Press, 2011: 3-9. 2 DONGT, YANGJ. A robust underdetermined mixing matrix estimation algorithm [J]. Journal of Astronautics, 2013, 34(3):426-433. 3 YANGM S, NATALIANIY. Robust-learning fuzzy C-means clustering algorithm with unknown number of clusters [J]. Pattern Recognition, 2017, 71: 45-59. 4 YANGQ, WANGL. The research on fuzzy clustering method based on different evolution algorithm in intrusion detection [J]. Applied Mechanics and Materials, 2014(651/652/653): 547-550. 5 LIC, ZHUL, LUOZ. Underdetermined blind source separation of adjacent satellite interference based on sparseness [J]. China Communications, 2017, 14(4): 140-149. 6 李虎,徐岩.基于GASA-FCM混合聚类与霍夫变换的欠定混合矩阵估计[J].计算机应用研究,2019,36(2):588-592. LIH, XUY. Underdetermined mixing matrix estimation algorithm using GASA-FCM based mixing clustering and Hough transform [J]. Application Research of Computers, 2019, 36(2): 588-592. 7 WUS, PANGY, SHAOS, et al. Advanced fuzzy C-means algorithm based on local density and distance [J]. Journal of Shanghai Jiaotong University (Science), 2018, 23(5): 636-642. 8 朱然,李积英.几种优化FCM算法聚类中心的方法对比及仿真[J].计算机技术与发展,2015,25(5):17-20. ZHUR, LIJ Y. Contrast and simulation of several clustering centers of optimized FCM algorithms [J]. Computer Technology and Development, 2015, 25(5): 17-20. 9 杨俊,卢琦.一种改进的进化规划算法及其应用[J].现代计算机(专业版),2009(5):32-35 YANGJ, LUQ. An improved evolutionary programming algorithm and its application [J]. Modern Computer, 2009(5): 32-35. 10 ROEBBERP. Adaptive evolutionary programming [J]. Monthly Weather Review, 2015, 143(5): 1497-1505. 11 王兆珍,刘旭鹏,杨淑莹.基于进化规划算法的图像聚类研究[J].天津师范大学学报（自然科学版）,2013,33(4):28-31. WANGZ Z, LIUX P, YANGS Y. Image clustering based on evolutionary programming algorithm [J]. Journal of Tianjin Normal University (Natural Science Edition), 2013, 33(4): 28-31. 12 邹云峰,张昕,宋世渊,等.基于局部密度的快速离群点检测算法[J].计算机应用,2017,37(10):2932-2937. ZOUY F, ZHANGX, SONGS Y, et al. Fast outlier detection algorithm based on local density [J]. Journal of Computer Applications, 2017, 37(10): 2932-2937. 13 DENGX, WANGL. Modified kernel principal component analysis using double-weighted local outlier factor and its application to nonlinear process monitoring [J]. ISA Transactions, 2018, 72: 218-228. 14 CHENP, PENGD, ZHENL, et al. Underdetermined blind separation by combining sparsity and independence of sources [J]. IEEE Access, 2017, 5: 21731-21742. 15 GUOQ, RUANG, QIL. A complex-valued mixing matrix estimation algorithm for underdetermined blind source separation [J]. Circuits, Systems, and Signal Processing, 2018, 37(8): 3206-3226. 16 毕晓君,宫汝江.基于混合聚类和网格密度的欠定盲矩阵估计[J].系统工程与电子技术,2012,34(3):614-618. BIX J, GONGR J. Underdetermined blind mixing matrix estimation algorithm based on mixing clustering and mesh density [J]. Systems Engineering and Electronics, 2012, 34(3): 614-618. 17 付宁,彭熹元.K-Hough欠定盲信道估计算法[J].电子测量与仪器学报,2008,22(5):63-67. FUN, PENGX Y. K-Hough underdetermined blind mixing model recovery algorithm [J]. Journal of Electronic Measurement and Instrument, 2008, 22(5): 63-67. 18 宫尚宝,郭玉翠.基于遗传算法的模糊聚类分析[J].模糊系统与数学,2010,24(6):123-128. GONGS B, GUOY C. Fuzzy clustering analysis based on genetic algorithms [J]. Fuzzy Systems and Mathematics, 2010, 24(6): 123-128. 19 刘沧生,许青林.基于密度峰值优化的模糊C均值聚类算法[J].计算机工程与应用,2018,54(14):153-157. LIUC S, XUQ L. Fuzzy C-means clustering algorithm based on density peak value optimization [J]. Computer Engineering and Applications, 2018, 54(14): 153-157.
2020, Vol. 40 
