Nonlinear control system with neural network controller using RasVal learning

Ning Shao, Kotaro Hirasawa, Masanao Ohbayashi, Kazuyuki Togo, Junichi Murata

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


In this paper, a new learning algorithm is applied to design of the optimal neural network controller of a nonlinear control system. The optimization method is named RasVal, it is a kind of random searching, and it can search for a global minimum systematically and effectively in a single framework which is not a combination of different methods. The searching for a global minimum based on the probability density functions of searching, which can be modified using information on success or failure of the past searching in order to execute intensified and diversified searching. By applying the proposed method to a nonlinear crane control system which can be controlled by the Universal Learning Network with the sigmoid functions, it has been shown that the RasVal is superior in performance to the commonly used back propagation learning algorithm.

Original languageEnglish
Pages (from-to)2859-2864
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 1997
EventProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA
Duration: Oct 12 1997Oct 15 1997

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture


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