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| 基于神经网络的动态响应时间序列预测 | |
| 引用本文： | 于霖冲,白广忱,焦俊婷. 基于神经网络的动态响应时间序列预测[J]. 微计算机信息, 2007, 23(16): 303-304 |
| 作者姓名： | 于霖冲 白广忱 焦俊婷 |
| 作者单位： | 1. 100083,北京,北京航空航天大学能源与动力工程学院;514015,嘉应,嘉应学院计算机科学系 2. 100083,北京,北京航空航天大学能源与动力工程学院 3. 514015,嘉应,嘉应学院计算机科学系 |
| 基金项目： | 国家自然科学基金;高等学校博士学科点专项科研项目;嘉应学院科研基金 |
| 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. |
| 关 键 词： | Elman神经网络 机构 动态响应 时间序列 预测 |
| 文章编号： | 1008-0570(2007)06-1-0303-02 |
| 修稿时间： | 2007-04-13 |
Time Series Prediction on Dynamical Responds Based on Artificial Neural Network |
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| YU LINCHONG,BAI GUANGCHEN,JIAO JUNTING. Time Series Prediction on Dynamical Responds Based on Artificial Neural Network[J]. Control & Automation, 2007, 23(16): 303-304 | |
| Authors: | YU LINCHONG BAI GUANGCHEN JIAO JUNTING |
| 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. |
| Keywords: | Elman neural network Mechanism Dynamic Response Time Series Prediction |
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