Neuro-Fuzzy Identifiers and Controllers

A neuro-fuzzy identifier for fuzzy modeling of a system is explained, and a control structure using this neurofuzzy identifier is proposed. The neuro-fuzzy identifier contains not only an adaptive clustering process for determining center points of the input and virtual output membership functions but also an adaptive process for deciding the shapes of the input membership functions. Moreover, linguistic fuzzy rules of a system can be obtained from the proposed neuro-fuzzy identifier, which can learn the initial implication fuzzy control rules of the system and then compensate for the error of the initial fuzzy control rules by a feedback control structure that maintains the stability of the system. Computer simulation shows that neuro-fuzzy identification is very effective in modeling fuzzy systems the fuzzy rules of which cannot be obtained easily and the neuro-fuzzy controller gives very effective control results by a learning process.

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