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ע⣺ѽlCеOӋ,2010,27(1):9-13,29l
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ꐕԴ1 Ԭ1 ǵ2żt3
(1.|AW Cе̌WԺ,Ϻ,201620 2.BW ӋCWԺ,B,116024 3.A|W Ԅӻϵ, Ϻ200237)

ժҪɱOӋ(DFCDesign For Cost)ǏOӋĽǶȽȫڳɱ(LCCLife Cycle Cost)OӋDFCĽǶͨ^õI܇OӋҪγߴlәCȅû񽛾Wjɷͨ^ӋڸOӋAξͿԹLCC齵LCC춨ҪAӋBP񽛾WjֵrքeLevenberg-MarquardtLMz㷨GAGenetic algorithmɷNӋYM񽛾WjɺĽYƷI܇IJָˣٹ܇|MAy
PI~ I܇ɱOӋ(DFCDesign For Cost)ȫڳɱ(LCCLife Cycle Cost)񽛾Wjz㷨

Performance targets and life cycle cost (LCC) estimation of family cars based on the neural network ensemble
Chen XiaoChuan , Yuan Jie1, Wu Di , Du Hongbin3
1) The Mechanical Engineering College, Donghua University, Shanghai 201620, China
2) Department of Intelligent Robotics, Dalian University of Technology, Dalian 116024, China
3) Department of Automation, East China University of Science and Technology, Shanghai 200237,China

AbstractDesign for cost (DFC) is a method that reduce Life cycle cost (LCC) at design stage. From the viewpoint of DFC, the design features of family cars were obtained, such as all dimensions, engine power and emission volume. At conceptual design stage, cars LCC were estimated using back propagation (BP) artificial nerve networks (ANN) method based on the features. An example was given. It is an important foundation in order to reduce LCC. Levenberg-MarquardtLMand Genetic algorithm (GA) were used to train BP ANNs weights. GAs results were better than LMs results. The results obtained through the adoptions of neural network ensemble are better than simply use GA or LM algorithm. Finally, performance targets (bodywork weight and oil/kilometers ) of family cars were predicted using BP ANN methods.
Keywords Design for cost (DFC), Life cycle cost (LCC), neural network ensemble, Genetic algorithm (GA)

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