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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