ISSN:
1662-9752
Source:
Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Based on the research of the functions of ANN-based cold extrusion process designsystem, genetic algorithm (GA) is proposed to optimize the topology and parameters of artificialneural networks (ANN), in order to improve the running efficiency of the networks. The binaryencoding approach is implemented to represent the GA chromosome. The code string or thechromosome was divided into three parts: the first part is the binary code of the cold extruded part; thesecond part is the binary code of the topology and parameters of ANN; the last is the binary code ofthe semi-cold-extruded-part or the billet. The 1/F(X) function is selected as the fitness function inGA, where, X represents the binary code of the cold extruded part, F(X) represents the error betweenthe real outputs of ANN and the desired results; the biased roulette wheel selection method is used forselecting operation in this paper; two-point crossover and one-point mutation are selected for thesetwo types of genetic operations. Finally, the typical cold extruded part is used for verification as anexample by using the optimized ANN, the result shows that ANN optimized by GA has efficiencyand validity in the cold extrusion process design system
Type of Medium:
Electronic Resource
URL:
http://www.tib-hannover.de/fulltexts/2011/0528/02/14/transtech_doi~10.4028%252Fwww.scientific.net%252FMSF.532-533.897.pdf
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