Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network

Abstract:

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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 et al., "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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Price:

$35.00

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