标题:Fuzzy C-Means Clustering Based Construction And Training For Second Order RBF Network
作者:Tyagi, Kanishka; Cai, Xun; Manry, Michael T.
通讯作者:Tyagi, K
作者机构:[Tyagi, Kanishka; Manry, Michael T.] Univ Texas Arlington, Dept Elect Engn, Arlington, TX 76010 USA.; [Cai, Xun] Shandong Univ, Sch Comp Sci & Techn 更多
会议名称:IEEE International Conference on Fuzzy Systems (FUZZ 2011)
会议日期:JUN 27-30, 2011
来源:IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)
出版年:2011
页码:248-255
DOI:10.1109/FUZZY.2011.6007713
关键词:Fuzzy-C means clustering; Hessian Matrix; Newton's Method; Optimal; Learning Factor; Orthogonal Least Square
摘要:The paper presents a novel two-step approach for constructing and training of optimally weighted Euclidean distance based Radial-Basis Function (RBF) neural network. Unlike other RBF learning algorithms, the proposed paradigms use Fuzzy C-means for initial clustering and optimal learning factors to train the network parameters (i.e. spread parameter and mean vector). We also introduce an optimized weighted Distance Measure (DM) to calculate the activation function. Newton's algorithm is proposed for obtaining multiple optimal learning factor for the network parameters (including weighted DM). Simulation results show that regardless of the input data dimension, the proposed algorithms are a significant improvement in terms of convergence speed, network size and generalization over conventional RBF models which use a single optimal learning factor. The generalization ability of the proposed algorithm is further substantiated by using k-fold validation.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:2
Scopus被引频次:4
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-80053072727&doi=10.1109%2fFUZZY.2011.6007713&partnerID=40&md5=1c0e694be1f030bb971d28e99e9d4ef1
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