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一篇文章应被ISTP检索 请有条件的虫子帮查下检索号，谢谢！！！ 题目：Application of Neural Networks Optimized by Genetic Algorithm in Forecasting Electric Field Aging Technics 会议：MMIT 2008 会议是在2008年12月开的 谢谢了！ [ Last edited by 努力着 on 2009-10-11 at 00:17 ] |
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runforaim(金币+1):谢谢参与
努力着(金币+2,VIP+0):感谢参与 10-11 00:16
runforaim(金币+1):谢谢参与
努力着(金币+2,VIP+0):感谢参与 10-11 00:16
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Application of Neural Networks Optimized by Genetic Algorithm in Forecasting Electric Field Aging Technics 作者: Zhan J (Zhan, Jun)1, Liu XF (Liu, Xiao-fang)1, Chen GM (Chen, Gui-ming)1, Zhang Q (Zhang, Qian)1 编者: Tan XZ 来源出版物: 2008 INTERNATIONAL CONFERENCE ON MULTIMEDIA AND INFORMATION TECHNOLOGY, PROCEEDINGS 页: 19-21 出版年: 2008 被引频次: 0 参考文献: 6 会议信息: 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 摘要: 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. 文献类型: Proceedings Paper 语言: English 作者关键词: artificial neural networks; back-propagation; genetic algorithm; electric field aging; prediction KeyWords Plus: AL-LI ALLOY 通讯作者地址: Zhan, J (通讯作者), Second Artillery Engn Inst, Xian, Peoples R China 地址: 1. Second Artillery Engn Inst, Xian, Peoples R China 出版商: IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA IDS 号: BLD58 ISBN: 978-0-7695-3556-2 |
2楼2009-10-09 20:17:47
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6楼2009-10-10 10:04:23
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7楼2009-10-10 11:25:56
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