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Time Series Prediction on Dynamical Responds Based on Artificial Neural Network

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【Author】 (Department of Computer Science & Technology, Jiaying University Meizhou 514015 China)Yu Linchong Jiao Junting (School of Jet Propulsion, Beijing University of Aeronautics & Astronautics Beijing 100083 China)Yu Linchong Bai Guangchen

【摘要】 The purpose of the research is to establish complicated nonlinear parameters time series prediction model.Considered in-fluence of friction and flexibleness,the characters of flexible mechanism are highly nonlinear.The dynamical responses of mechanism become more uncertain because of those influence.It is more difficult to control the mechanism at the real time.Via improved El-man dynamical artificial neural network,the prediction model was established to forecast kinetic parameters.A flexible mechanism example was applied to test this method.The results proved that the calculate speed of the model was fast and the precision was high.The method provided an available way on controlling at the real time for complicated large systems.

【Abstract】 The purpose of the research is to establish complicated nonlinear parameters time series prediction model.Considered in-fluence of friction and flexibleness,the characters of flexible mechanism are highly nonlinear.The dynamical responses of mechanism become more uncertain because of those influence.It is more difficult to control the mechanism at the real time.Via improved El-man dynamical artificial neural network,the prediction model was established to forecast kinetic parameters.A flexible mechanism example was applied to test this method.The results proved that the calculate speed of the model was fast and the precision was high.The method provided an available way on controlling at the real time for complicated large systems.

【基金】 National Natural Science Foundation of China (50275006);Chinese Ministry of Education Foundation for doctorcandidate (20020006036);Research Foundation of JiayingUniversity (06KJ23)
  • 【文献出处】 微计算机信息 ,Control & Automation , 编辑部邮箱 ,2007年16期
  • 【分类号】TP183
  • 【被引频次】2
  • 【下载频次】21
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