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- W2391730093 abstract "Neural network has been one of effective tools in dealing with non-linear system recognition problem.However,the common multilayer perceptron has some faults,such as instability,and low convergence velocity.Based on multilayer perceptron and Fourier series,a kind of neural network,named Fourier neural network,is proposed.Compared with traditional multilayer perceptron,Fourier neural network has better pattern classification ability and generalization property.Since the existent Fourier neural network adopts method of steepest descent which induces the problems of local minimum and low learning velocity,it constructs an improved Fourier neural network based on the double dogleg step method and utilizes this network in dealing with non-linear system recognition problem.The double dogleg step method avoids the local minimum problem and has two-order convergence velocity.Several simulation examples are utilized to validate the performance of the improved network,and the results are compared with some results obtained from several other classical neural networks.Meanwhile the new network is applied to solve non-linear system recognition problem compared with results from other methods." @default.
- W2391730093 created "2016-06-24" @default.
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- W2391730093 date "2012-01-01" @default.
- W2391730093 modified "2023-09-23" @default.
- W2391730093 title "Novel Fourier neural network using double dogleg step method" @default.
- W2391730093 hasPublicationYear "2012" @default.
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