A novel hybrid neural fuzzy inference system is presented. Only based on the desired input-output data pairs, are the knowledge acquisition and initial fuzzy rule sets available. Then, employing neural networks learning techniques, the fuzzy logic rules,input-output fuzzy membership functions and weights in networks can be easily tuned. So the rule matching is reduced ,inferencing is accelerated, adaptability of the system is greatly improved. To illustrate the performance of the proposed neuro-fuzzy hybrid model, simulations on the chaotic Mackey-Glass time series prediction are performed. Combining either off-line or on-line learning with the proposed hybrid model, we can show that the chaotic Mackey-Glass time series are accurately predicted, and demonstrate the effectivness of the model.
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