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An Adaptive Classifier Based on Artificial Immune Network.
- 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