대한기계학회논문집A (Transactions of the Korean Society of Mechanical Engineers A)

  • 제33권8호
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  • Pages.753-759
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  • 2009
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  • 1226-4873(pISSN)
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  • 2288-5226(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
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웨이블렛 신경망을 이용한 전역근사 메타모델의 성능비교

Global Function Approximations Using Wavelet Neural Networks

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

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.

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참고문헌

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피인용 문헌

  1. Development of Computational Orthogonal Array based Fatigue Life Prediction Model for Shape Optimization of Turbine Blade vol.34, pp.5, 2010, https://doi.org/10.3795/KSME-A.2010.34.5.611