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.

• Periodical: Key Engineering Materials (Volumes 353-358)
• Pages: 1029-1032
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.353-358.1029
• Online since: September 2007
• Authors: Chao Hua Fan, Yu Ting He, Heng Xi Zhang, Hong Peng Li, Feng Li
• Keywords: BP Neural Network (BPNN), Fatigue Performance, Genetic Algorithm (GA), Predictive Model

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