Abstract
A reinforcemen-based fuzzy neural network control with automatic rule generation (RBFNNC) is proposed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based on the state variables of object system. RBFNNC was applied to a cart-pole balancing system and simulation result shows significant improvements on the rule generation.
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Project supported by the Science Foundation of Shanghai Municipal Commission of Education (980034)
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Wu, Gf., Dong, Jq., Chen, Ym. et al. Reinforcement-based fuzzy neural network control with automatic rule generation. J. of Shanghai Univ. 3, 282–286 (1999). https://doi.org/10.1007/s11741-999-0005-8
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DOI: https://doi.org/10.1007/s11741-999-0005-8