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An Adaptive Classifier Based on Artificial Immune Network.

Authors :
Istrail, Sorin
Pevzner, Pavel
Waterman, Michael S.
Kang Li
Xin Li
Irwin, George William
Gusen He
Zhiguo Li
Jiang Zhong
Yong Feng
ZhongFu Wu
Source :
Life System Modeling & Simulation; 2007, p422-428, 7p
Publication Year :
2007

Abstract

The central problem in training a radial basis function neural network is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose a new method to construct an adaptive RBF neural network classifier based on artificial immune network algorithm. A multiple granularities immune network (MGIN) algorithm is employed to get the candidate hidden neurons and construct an original RBF network including all candidate neurons, and a removing redundant neurons procedure is used to simplify the classifier finally. Some experimental results show that the network obtained tends to generalize well. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540747703
Database :
Complementary Index
Journal :
Life System Modeling & Simulation
Publication Type :
Book
Accession number :
33169839
Full Text :
https://doi.org/10.1007/978-3-540-74771-0_48