Reinforcement-driven adaptation of control relations

The conceptual framework of a hybrid control system architecture is briefly discussed. It employs neural and fuzzy techniques side-by-side using each one for the task to which it is best suited. Our main interest is with the adaptation of the fuzzy control knowledge. The adaptation algorithm is based on reinforcement signals and directly optimizes the global fuzzy relation representing the complete knowledge base. The new approach is experimentally evaluated.

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