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《Journal of Beijing Institute of Machinery》 2009-01
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Condition development prediction of rotating machine based on chaos-neural networks

ZHU Chun-mei1,2,XU Xiao-li1,2,ZHANG Jian-min2(1.School of Electromechanical Engineering,Beijing Information Science and Technology University,Beijing 100192,China;2.School of Mechanical and Vehicular Engineering,Beijing Institute of Technology,Beijing 100081,China)  
Condition development prediction is indispensable to the safe operation of rotating machines.The typical signal of rotating machine is nolinear and non-stationary.So the traditional signal analysis methods are not suitable for these signals.Introducing chaos theory into condition development prediction of rotating machine and aiming at the industrial smokes and gas turbine,the method of chaos forecasting and the forecasting theory based on chaos-neural networks are elaborated.The paper finishes a prediction based on the chaos-neural networks and compares it with the gray predicting method.The result shows that the prediction based on the chaos-neural networks has a higher accuracy and effectiveness.
【Fund】: 国家自然科学基金(50375017);; 北京市自然科学基金(3062008)
【CateGory Index】: TH17
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