Paper Title:
Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network
  Abstract

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. At the same time, a fuzzy-neural network method is established for the same purpose. The results indicate that genetic algorithm-neural network and fuzzy-neural network can both be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely.

  Info
Periodical
Advanced Materials Research (Volumes 33-37)
Edited by
Wei Yang, Mamtimin Geni, Tiejun Wang and Zhuo Zhuang
Pages
1283-1288
DOI
10.4028/www.scientific.net/AMR.33-37.1283
Citation
C. H. Fan, Y. T. He, H. P. Li, F. Li, "Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network", Advanced Materials Research, Vols. 33-37, pp. 1283-1288, 2008
Online since
March 2008
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