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1 July 1992 Reinforcement and unsupervised learning in fuzzy-neuro controllers
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Abstract
Refinement of the performance of approximate reasoning based controllers (e.g., fuzzy logic based controllers) by using reinforcement (also known as graded) learning have been proposed recently. However, reinforcement learning schemes known today have problems in learning and generating proper control inputs, especially, for complex plants. In this paper, we have presented novel schemes to alleviate these problems found in the existing reinforcement learning based controllers by using unsupervised learning and neuro-fuzzy approach.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Emdad Khan "Reinforcement and unsupervised learning in fuzzy-neuro controllers", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140124
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