New enhanced methods for radial basis function neural networks in function approximation

被引：0
|
作者：
Fatemi, M [1 ]
Roopaei, M [1 ]
Shabaninia, F [1 ]
机构：
[1] Shiraz Univ, Dept Elect Engn, Shiraz, Iran
来源：
HIS 2005: 5th International Conference on Hybrid Intelligent Systems, Proceedings | 2005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：
Function Approximation is a widely used method in System Identification and recently RBF networks have been proposed as powerful tools for that. Existing algorithms suffer from some restrictions such as slow convergence and/or encountering to bias in parameter convergence. This paper is an attempt to improve the above problems by proposing new methods of parameter initializing and post-training to reach better capabilities in learning time and desired precision compared to previous RBF networks.
引用
收藏
页码：524 / 527
页数：4
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