Global Function Approximations Using Wavelet Neural Networks

웨이블렛 신경망을 이용한 전역근사 메타모델의 성능비교

  • 신광호 (연세대학교 대학원 기계공학과) ;
  • 이종수 (연세대학교 기계공학부)
  • Published : 2009.08.01


Feed-forward neural networks have been widely used as function approximation tools in the context of global approximate optimization. In the present study, a wavelet neural network (WNN) which is based on wavelet transform theory is suggested as an alternative to a traditional back-propagation neural network (BPN). The basic theory of wavelet neural network is briefly described, and approximation performance is tested using a nonlinear multimodal function and a composite rotor blade analysis problem. Laplacian of Gaussian function, Mexican function, and Morlet function are considered during the construction of WNN architectures. In addition, approximation results from WNN are compared with those from BPN.


Global Function Approximation;Feed-Forward Neural Networks;Wavelet Neural Network;Wavelet Transform


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