NDLI: An optimizing method of RBF neural network based on genetic algorithm

Content Provider	Springer Nature Link
Author	Ding, Shifei Xu, Li Su, Chunyang Jin, Fengxiang
Copyright Year	2011
Abstract	In the traditional learning algorithms of radial basis function (RBF) neural network, the architecture of the network is hard to be decided; thereby, the learning ability and generalization ability are hard to achieve optimal. In this paper, we propose an algorithm to optimize the RBF neural network learning based on genetic algorithm; it uses hybrid encoding method, that is, encodes the network by binary encoding and encodes the weights by real encoding; the network architecture is self-adapted adjusted, and the weights are learned. Then, the network is further adjusted by pseudo inverse method or least mean square method. Experiments prove that the network gotten by this method has a better architecture and stronger classification ability, and the time of constructing the network artificially is saved. The algorithm is a self-adapted and intelligent learning algorithm.
Starting Page	333
Ending Page	336
Page Count	4
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	2
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-07-14
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Genetic algorithm (GA) Radial basis function (RBF) Network structure Network weight Algorithm Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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An optimizing method of RBF neural network based on genetic algorithm