Global function approximations using wavelet neural networks

Kwang Ho Shin, Jongsoo Lee

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)753-759
Number of pages7
JournalTransactions of the Korean Society of Mechanical Engineers, A
Issue number8
Publication statusPublished - 2009 Aug

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering


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