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Application of improved BP neural network in structural design of drive shaft

Wang Sheng;Yang Hongcai;Mechanical and Electrical Engineering Department of Xijing University;  
The Shaffer function defines the domain in the range of [-10,10], BP neural network fitting function costs long training time, and can not achieve the desired accuracy, it showed that the ability of BP neural network to fit the complicated nonlinear functions needs to be improved. This paper presents an improved BP neural network, first, we should carry out K-Means clustering for the input of network, BP neural network is trained by large scale nodes, partial nodes are activated by clustering input to train, each cluster uses different nodes, by sub network to train clustering samples, it reduces network fitting difficulty. The test results show that improved BP neural network meets the precision. Finally, the axle diameter is calculated optimally by the improved BP neural network.
【Fund】: 西京学院2016年院科研基金;项目名称:粒子群算法在机器人循迹控制中的应用;项目编号:XJ160232;;项目名称:西京学院2017年创新创业训练计划项目;项目编号:127152017036
【CateGory Index】: TP183;TS803.6
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Chinese Journal Full-text Database 4 Hits
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1 LIU Yi;ZHANG Kun;School of Computer Science and Technology,Nanjing University of Science and Technology;;Short-Term Traffic Flow Forecasting Based on Wavelet Denoising and GA-Elman Neural Network[J];交通科技与经济;2015-06
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3 CAO Li;TANG Ling;WU Hao;GAO Xiang;YUE Yinggao;School of Automation and Electronic Information,Sichuan University of Science & Engineering;School of Mechanical Engineering,Sichuan University of Science & Engineering;School of Instrument Science and Engineering,Southeast University;;Research on Short-term Freeway Traffic Flow Prediction Based on Improved Wavelet Neural Network[J];四川理工学院学报(自然科学版);2015-06
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