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

Chao Hua Fan; Yu Ting He; Hong Peng Li; Feng Li

doi:10.4028/www.scientific.net/AMR.33-37.1283

Paper Titles

Vibration Characteristic Prediction of a Heavy Machinery Suspension Seat Using Multi-Body Dynamic Analysis Method
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Erosion Properties of Molybdenum Coatings Sprayed by Electro-Thermal Explosion Ultra-High Speed Spraying
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Study on Erosion-Resistance of Electro-Thermal Explosion Ultra-High Speed Spraying MoSi₂-Based Coatings
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Erosion Behavior of TiC-xNi Composite Coatings In Situ Synthesized by Electro-Thermal Explosion Ultra-High Speed Spraying
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Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network
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Non-Destructive Inspection of Rebar Corrosion in Concrete Structures by Using BEM and GA
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Nondestructive Corrosion Monitoring in Reinforced Concrete by Boundary Element Inverse Analysis
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Effective BEM for Corrosion Analysis of Net Structure
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Numerical Simulation of Corrosion under Different Solution Velocity
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 33-37Performance Prediction of Pre-Corroded Aluminum...

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.