A New Control Approach Based on Diagonal Recurrent Neural Network for Heavy Rail Tilter
This paper introduces a new approach based on artificial neural networks for a heavy rail tilter control system. The diagonal recurrent neural network (DRNN) type is used for such a purpose. An DRNN-based discrete event controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an DRNN-based discrete event controller are presented. The proposed controller is tested by real- time experiment. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented, the experment results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.
heavy rail tilter control system DRNN discrete event control
Fuquan Tu Kuisheng Chen Jianxun Cheng
College of Machinery & Automation, Wuhan University of Science and Technology, China College of Computer, Wuhan University of Science and Technology, China
国际会议
2006现代科技国际研讨会(The International Workshop on Modern Science and Technology in 2006)
北京
英文
471-475
2006-04-01（万方平台首次上网日期，不代表论文的发表时间）