ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM
来源期刊：Acta Metallurgica Sinica2003年第6期
论文作者：W.X.Zhu X.C.Li K.M.Chen D.S.Mei G.Chen J.Zhang
Key words：artificial neural network; expert system; hybrid intelligent system; gear materials selection;
Abstract: An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.
W.X.Zhu1，X.C.Li2，K.M.Chen4，D.S.Mei5，G.Chen2，J.Zhang3
(1.School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China;
2.Department of Materials Engineering, Nanjing University of Science and Technology, Nanjing 210014,China;
3.School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China;
4.School of Materials Engineering, Jiangsu University, Zhenjiang 212013, China;
5.Iron and Steel Research Institute, Panzhihua Iron and Steel Group, Panzhihua 617000, China)
Abstract:An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.
Key words:artificial neural network; expert system; hybrid intelligent system; gear materials selection;
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