Reinforcement and unsupervised learning in fuzzy-neuro controllers
Emdad Khan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
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.