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汽车半主动悬架的神经网络控制

邱 浩1 熊 智2   

  1. 1．深圳职业技术学院 汽车与交通学院，广东 深圳 518055；
    2．南京航空航天大学 导航研究中心，江苏 南京 210016
  • 收稿日期:2007-07-09 修回日期:2007-11-14 出版日期:2008-01-31 发布日期:2008-01-31

Neural Network Control for Semi-active Suspension Automobile

Qiu Hao1, Xiong Zhi2   

  1. 1. School of Automotive and Transportation, Shenzhen Polytechnic College, Shenzhen 518055, China;
    2. Research Center of Navigation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2007-07-09 Revised:2007-11-14 Online:2008-01-31 Published:2008-01-31

被引次数

10

摘要： 针对目前标准BP神经网络的缺点，提出基于高阶导数的多记忆BP算法，将能量函数的 阶导数与最速下降方向相结合，构造出一个新的最速下降方向，从而提高了神经网络的学习速度。证明了该算法相对于传统梯度算法的快速性，然后给出了该算法的实现方法，并进行了算例仿真。为了证明其实效性，设计了汽车半主动悬架神经网络控制器。结果证明，该算法便捷、实用、有效。

关键词: 神经网络, BP算法, 高阶导数 , 悬架

Abstract: Regarding drawbacks of the standard BP algorithm, a high-order derivative based multiple memory BP algorithm is proposed. It combines the n-th order of energy function with the direction of the fastest decline to construct a new direction of the fastest decline, and improve the learning speed of the neural network. The new algorithm is compared with the traditional gradient algorithm to show its high computation speed. Implementation of the new algorithm is given. Finally a neural network controller is designed to optimize the performance of the automobile suspension. The result shows that the new algorithm is convenient and effective.

Key words: neural network, BP algorithm, high order derivative, suspension