Radial basis function neural networks: Theory and applications

被引:0
作者
Strumillo, P [1 ]
Kaminski, W [1 ]
机构
[1] Tech Univ Lodz, Inst Elect, PL-90924 Lodz, Poland
来源
NEURAL NETWORKS AND SOFT COMPUTING | 2003年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Essential theory and main applications of feed-forward connectionist structures termed radial basis function (RBF) neural networks are given. Universal approximation and Cover's theorems are outlined that justify powerful RBF network capabilities in function approximation and data classification tasks. The methods for regularising RBF generated mappings are addressed also. Links of these networks to kernel regression methods, density estimation, and nonlinear principal component analysis are pointed out. Particular attention is put on discussing different RBF network training schemes, e.g. the constructive method incorporating orthogonalisation of RBF kernels. Numerous, successful RBF networks applications in diverse fields such as signal modelling, non-linear time series prediction, identification of dynamic systems, pattern recognition, and knowledge discovery are outlined.
引用
收藏
页码:107 / 119
页数:13
相关论文
共 39 条
[1]  
AJZERMAN MA, 1976, IMAGE RECOGNITION ME
[2]  
[Anonymous], IEEE T NEURAL NETWOR
[3]  
BELICZYNSKI B, 2000, INCREMENTAL FUNCTION
[4]   RADIAL BASIS FUNCTION NETWORK CONFIGURATION USING GENETIC ALGORITHMS [J].
BILLINGS, SA ;
ZHENG, GL .
NEURAL NETWORKS, 1995, 8 (06) :877-890
[5]  
Broomhead D. S., 1988, Complex Systems, V2, P321
[6]   ORTHOGONAL LEAST-SQUARES LEARNING ALGORITHM FOR RADIAL BASIS FUNCTION NETWORKS [J].
CHEN, S ;
COWAN, CFN ;
GRANT, PM .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1991, 2 (02) :302-309
[7]   A CLUSTERING TECHNIQUE FOR DIGITAL-COMMUNICATIONS CHANNEL EQUALIZATION USING RADIAL BASIS FUNCTION NETWORKS [J].
CHEN, S ;
MULGREW, B ;
GRANT, PM .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1993, 4 (04) :570-579
[8]   Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks [J].
Chen, S ;
Wu, Y ;
Luk, BL .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1999, 10 (05) :1239-1243
[10]  
CICHOSZ P, 2000, SYSTEMS THAT LEARN