Analysis of algorithms for radial basis function neural network

被引：0

作者：

Stastny, Jiri ^{[1
]}

Skorpil, Vladislav ^{[2
]}

机构：

[1] Brno Univ Technol, Dept Comp Sci & Automat, Purkynova 118, Brno 61200, Czech Republic

[2] Brno Univ Technol, Dept Telecommun, Bron, France

来源：

PERSONAL WIRELESS COMMUNICATIONS | 2007年 / 245卷

关键词：

radial basis function; learning algorithm; neuron; hidden layer;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper describes the analysis of algorithms for the hidden layer construction of network and for learning of the Radial Basis Function neural Network (RBFN). We compared results obtained by using of learning algorithms LMS (Least Mean Square) and Gradient Algorithms (GA) and results are obtained by using of algorithms APC-III and K-means for hidden layer contruction of neural network. The principles and algorithms given below have been used in an application for object classification that was developed at Brno University of Technology. This solution is suitable for the research of personal wireless communications and similar systems.

引用

页码：54 / +

页数：3

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