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Intelligent control based on fuzzy logic and neural net theoryIn the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.
Document ID
Document Type
Conference Paper
Lee, Chuen-Chien (California Univ. Berkeley, CA, United States)
Date Acquired
September 6, 2013
Publication Date
February 1, 1991
Publication Information
Publication: NASA, Lyndon B. Johnson Space Center, Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, Volume 2
Subject Category
Funding Number(s)
Distribution Limits
Work of the US Gov. Public Use Permitted.

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