Paper Title:
Predictive Model Based on Genetic Algorithm-Neural Network for Fatigue Performances of Pre-Corroded Aluminum Alloys
  Abstract

In the paper, genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada, and the fatigue performances of pre-corroded aluminum alloys can be predicted. The results indicate that genetic algorithm-neural network algorithm can be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely, compared with traditional neural network.

  Info
Periodical
Key Engineering Materials (Volumes 353-358)
Edited by
Yu Zhou, Shan-Tung Tu and Xishan Xie
Pages
1029-1032
DOI
10.4028/www.scientific.net/KEM.353-358.1029
Citation
C. H. Fan, Y. T. He, H. X. Zhang, H. P. Li, F. Li, "Predictive Model Based on Genetic Algorithm-Neural Network for Fatigue Performances of Pre-Corroded Aluminum Alloys", Key Engineering Materials, Vols. 353-358, pp. 1029-1032, 2007
Online since
September 2007
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Price
$32.00
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