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Application of Neural Networks Optimized by Genetic Algorithm in Forecasting Electric Field Aging Technics ×÷Õß: Zhan J (Zhan, Jun)1, Liu XF (Liu, Xiao-fang)1, Chen GM (Chen, Gui-ming)1, Zhang Q (Zhang, Qian)1 ±àÕß: Tan XZ À´Ô´³ö°æÎï: 2008 INTERNATIONAL CONFERENCE ON MULTIMEDIA AND INFORMATION TECHNOLOGY, PROCEEDINGS Ò³: 19-21 ³ö°æÄê: 2008 ±»ÒýƵ´Î: 0 ²Î¿¼ÎÄÏ×: 6 »áÒéÐÅÏ¢: International Conference on MultiMedia and Information Technology Three Gorges, PEOPLES R CHINA, DEC 30-31, 2008 Int Sci & Engn Ctr; Intelligent Informat Technol Application Assoc ÕªÒª: In the study, back-propagation neural networks(BP-NN) theory and genetic algorithm(GA) were used to build a nonlinear prediction model reflecting the relationship between technics parameters of electric field aging and mechanical properties of LY12 aluminum alloy. In this model, electric field intensity, aging temperature and time were as input parameters. Tensile strength, yield strength and micro-yield strength were as output parameters. The result shows that BP-NN model has good training ability whose error was less than 0.1%. The maximal error of BP-NN model for forecasting the mechanical properties under selected technics was close to 10%. Using genetic algorithm to optimize BP-NN (GA-BP) can not increase the training ability which had a higher training error in the condition of less experiment datas., but GA-BP model can improve the prediction ability of BP-NN model and the maximal prediction error was less than 4% which lied at rational range. GA-BP model can be used to optimize technics parameters and decrease experimental work and cost which is a new method for studying electric field aging technics. ÎÄÏ×ÀàÐÍ: Proceedings Paper ÓïÑÔ: English ×÷Õ߹ؼü´Ê: artificial neural networks; back-propagation; genetic algorithm; electric field aging; prediction KeyWords Plus: AL-LI ALLOY ͨѶ×÷ÕßµØÖ·: Zhan, J (ͨѶ×÷Õß), Second Artillery Engn Inst, Xian, Peoples R China µØÖ·: 1. Second Artillery Engn Inst, Xian, Peoples R China ³ö°æÉÌ: IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA IDS ºÅ: BLD58 ISBN: 978-0-7695-3556-2 |
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