DocumentCode :
2739247
Title :
Neural networks with node gates
Author :
Myint, H.M. ; Murata, J. ; Nakazono, T. ; Hirasawa, K.
Author_Institution :
Graduate Sch. of Inf. Sci. & Electr. Eng., Kyushu Univ., Fukuoka, Japan
fYear :
2000
fDate :
2000
Firstpage :
253
Lastpage :
257
Abstract :
Function approximation problems for ordinary neural networks may be rather difficult, if the function becomes complicated, due to the necessity of big network size and the possibilities of many local minima. A promising way to solve these difficulties is the localization of the problem. According to this concept, a new architecture of a neural network is proposed namely neural network with node gates. In the paper, a function approximation example is provided to demonstrate the better performance of the proposed network than the ordinary neural network
Keywords :
feedforward neural nets; function approximation; learning (artificial intelligence); multilayer perceptrons; neural net architecture; local minima; localization; node gates; Artificial neural networks; Biological neural networks; Brain modeling; Feedforward neural networks; Feeds; Function approximation; Fuzzy neural networks; Information science; Neural networks; Transfer functions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robot and Human Interactive Communication, 2000. RO-MAN 2000. Proceedings. 9th IEEE International Workshop on
Conference_Location :
Osaka
Print_ISBN :
0-7803-6273-X
Type :
conf
DOI :
10.1109/ROMAN.2000.892504
Filename :
892504
Link To Document :
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