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Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environmentWhereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.
Document ID
19910012472
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Hayashi, Isao (Matsushita Electric Industrial Co. Ltd. Moriguchi, Japan)
Nomura, Hiroyoshi (Matsushita Electric Industrial Co. Ltd. Moriguchi, Japan)
Wakami, Noboru (Matsushita Electric Industrial Co. Ltd. Moriguchi, Japan)
Date Acquired
September 6, 2013
Publication Date
February 1, 1991
Publication Information
Publication: NASA, Lyndon B. Johnson Space Center, Proceedings of the 2nd Joint Technology Workshop on Neural Networks and Fuzzy Logic, Volume 1
Subject Category
Cybernetics
Accession Number
91N21785
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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