An Adaptive Wavelet Frame Neural Network Method for Efficient Reliability Analysis
| Autor: | Hongzhe Dai, Wei Wang, Guofeng Xue |
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| Rok vydání: | 2014 |
| Předmět: |
Radial basis function network
Artificial neural network business.industry Computer science Frame (networking) Activation function ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Cascade algorithm Machine learning computer.software_genre Computer Graphics and Computer-Aided Design Constructive Computer Science Applications Wavelet Computational Theory and Mathematics Artificial intelligence business computer Reliability (statistics) Civil and Structural Engineering |
| Zdroj: | Computer-Aided Civil and Infrastructure Engineering. 29:801-814 |
| ISSN: | 1093-9687 |
| DOI: | 10.1111/mice.12117 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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