類神經模糊系統結合了類神經網路和模糊系統的優點已經被廣泛應用在各個不同的領域，例如追蹤控制、系統識別、人因工程、震動控制等等，而且都已經有很好的成效。 其中，主要有兩個模糊規則加入類神經模糊系統中：Mamdani和Sugeno的模糊規則。然而，在所有參考的文獻當中，各個文獻所展示的都只有個別單一的規則，而沒有同一個例子分別應用兩種不同模糊規則的文獻。基於想要知道兩種不同規則應用在同一個例子的效果，本文做了一個非線性系統比較的動作。參考Yang et al.(2007)發表的文獻，兩種不同模糊規則的類神經模糊系統都以五層的結構來建構。經由比較的結果，發現Sugeno的類神經模糊系統有較好的逼近效果，因此其被用來做為預測的系統來預測國人的旅遊人數，其中四個因素被用來作為影響因子:國人的年收入、與各國的匯率、各國的消費者物價指數以及歷年來國人去各國的人數趨勢，而結果再度顯示類神經模糊系統有著很好的預測效果。

The neuro-fuzzy system integrated by neural network and fuzzy system with the capability of adjusting the membership functions and providing the fuzzy inference rules has been proposed in system identification and control. The neuro-fuzzy system in the study has the framework of a five-layer feedforward network configuration: the input layer, the fuzzifier layer, the inference layer, the defuzzifier layer and the output layer. Both Sugeno and Mamdani fuzzy rules are employed in the neuro-fuzzy system to compare their performance in a bench-market nonlinear equation. It is shown that the neuro-fuzzy system with Sugeno fuzzy rules has better accuracy in system identification. Application to forecasting shows that the neuro-fuzzy system has better prediction than neural network. The prediction also offers sensitivity analysis of the simulating data.

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