Complex System Pattern Recognition Method Based on Adaptive Wavelet Neural Network

To solve the training efficiency and accuracy bottleneck problems of the traditional neural network method in modeling and identification application of complex systems,an adapted wavelet neural network(AWNN) method was proposed. First,adapted and integrated layers were design to make AWNN create normalization parameter to adapt the sample data. AWNN absorbed the advantages of BP neural network,RBF neural network and wavelet analysis algorithm overcome the problems of the original neural network. A large number of experiments and comparative analysis had been implemented to verify the performance and characteristics of the AWNN. Both computer simulation results and intelligent video analysis application experiments show that the AWNN method has faster convergence speed,higher accuracy and better robustness.