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Design and Application of WRBF Neural Network Based on Improved GA


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Design and Application of WRBF Neural Network Based on Improved GA
CHEN De-bao12ZHAO Chun-xia1
1.School of Computer Science and Technology,NUST,Nanjing 210094,China;2.Physical Department,Huaibei Coal Industry Teachers College,Huaibei 235000,China
w ave let netwo rk rad ial basis function netw ork w avelet radial basis function network genet ic algo rithm
In o rder to overcome the demanding samples of design ing RBF ( rad ial basis funct ion ne-t w ork) neural netw ork and w avelet netw ork automatically and the overlap phenomenon of linear summat ion in output layer, a new four-layer netw ork namedWRBF ( w ave let radia l basis function ne-t w ork) is designed based on the simple structures of RBF and w avelet netw ork. In the netw ork, the first h idden layer is used to compress input space and the second h idden layer is to process the compressed variables. In training m ethods, a h igh-rank chromosome for indiv idual in improved GA is used to realize selecting connection betw een tw o h idden layers. The structure and parameters o f WRBF netw ork are optim ized simu ltaneously. The experimental results from M ISO system and coeff-i cient model o f therma lm eter demonstrate that the improved GA reduces the premature phenomenon and that theWRBF netw ork has h igher prec ision formode ling.


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Last Update: 2007-06-30