類神經模糊系統在工業上已被廣泛的使用，然而其誤差的判斷的標準是使用易陷入局部最小值的誤差倒傳遞法。本文整合類神經模糊理論與基因演算法，充分利用基因演算法所擁有的多目標收尋問題以及能產生全域最佳值的特性來調整歸屬函數，改善上述的問題並應用在系統識別上。最後可以從模擬系統識別的結果也顯示整合類神經模糊理論與基因演算法較優於類神經模糊統。因此，為了工程上之實現，有效的減少系統誤差是必要的。

It is known that the training process of a neuro-fuzzy system is easily stuck in local minimum, the purpose of this work is to apply genetic algorithm to tune the weights and the membership functions of a neuro-fuzzy system. In this integrated system, the weighting values of neural network will be coded as genetic population size in binary genetic algorithm. In hence, the fitness function can be defined to calculate the optimizing weighting values. The optimizing weight values can be obtained from simulation result, and the error of simulation can also be reduced. Integrating neuro-fuzzy system and genetic algorithm is shown to have better performance in system identification.

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