A study of functions distribution of neural networks

Author

XIONG, Qingyu ; Hirasawa, Kotaro ; Hu, Jinglu ; Murata, Junichi

Author_Institution

Intelligent Control Lab., Kyushu Univ., Fukuoka, Japan

Volume

4

fYear

2001

fDate

2001

Firstpage

2361

Abstract

In this paper, universal learning networks with branch control of relative strength (ULNs with BR) is studied which consists of basic networks and branch control networks. The branch control net work can be used to determine which intermediate nodes of the basic network should be connected to the output node with a coefficient of relative strength ranging from zero to one. This determination will adjust the outputs of the intermediate nodes of the basic network. Therefore, by using ULNs with BC, locally functions distributed networks can be realized depending on the values of the inputs of the network. ULNs with BR is applied to a two-spirals problem. The simulation results show that ULNs with BC exhibits better performance than the conventional networks with comparable complexity

Keywords

feedforward neural nets; learning (artificial intelligence); branch control; distributed network; functions distribution; multilayer neural networks; universal learning networks; Artificial neural networks; Biological neural networks; Brain modeling; Information science; Intelligent control; Laboratories; Large-scale systems; Marine vehicles; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.938735

Filename

938735