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An Adaptive Wavelet Frame Neural Network Method for Efficient Reliability Analysis
Author(s) -
Dai Hongzhe,
Xue Guofeng,
Wang Wei
Publication year - 2014
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12117
Subject(s) - wavelet , frame (networking) , computer science , reliability (semiconductor) , artificial neural network , artificial intelligence , cascade algorithm , wavelet transform , pattern recognition (psychology) , algorithm , wavelet packet decomposition , telecommunications , power (physics) , physics , quantum mechanics
Abstract Artificial neural networks (ANNs) method is widely used in reliability analysis. However, the performance of ANNs cannot be guaranteed due to the fitting problems because there is no efficient constructive method for choosing the structure and the learning parameters of the network. To mitigate these difficulties, this article presents a new adaptive wavelet frame neural network method for reliability analysis of structures. The new method uses the single‐scaling multidimensional wavelet frame as the activation function in the network to deal with the multidimensional problems in reliability analysis. Because the wavelet frame is highly redundant, the time–frequency localization and matching pursuit algorithm are respectively utilized to eliminate the superfluous wavelets, thus the obtained wavelet frame neural network can be implemented efficiently. Five examples are given to demonstrate the application and effectiveness of the proposed method. Comparisons of the new method and the classical radial basis function network method are made.