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《China Mechanical Engineering(中国机械工程)》 2012-01
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Linear Servo System Control Based on Improved Elman Neural Network with Sparse Memory

Zuo Jianmin1,2 Pan Chao1,3 Wang Mulan4 1.Jiangsu University,Zhenjiang,Jiangsu,212013 2.Jiangsu Teachers University of Technology,Changzhou,Jiangsu,213001 3.Shanghai Railway Administration,Shanghai,200071 4.Nanjing Institute of Technology,Nanjing,211167  
In order to improve the dynamic performance and disturbance resistance abilities of CNC machine tool direct-drive linear servo system with complexities of dynamics and nonlinear,taking advantages of historical control information of repeated motion,an improved Elman neural network with spares memory was proposed and rapid associate theory based on table-look up was introduced to enhance learning speed and generalization capability of neural network.The information of neural network was classified,stored and selected to use.The mathematical model of improved Elman neural network and the weight adjustment algorithm were derived in detail and applied to a linear feed servo system.The results show that the controller based on improved Elman neural network with spares memory exhibits satisfactory performance in tracking precision and disturbance resistance.
【Fund】: 江苏省“六大人才高峰”高层次人才项目(2008163);; 江苏省高校自然科学基础研究项目(08KJB460003)
【CateGory Index】: TG659
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