梯形歸屬函數之模糊系統的自動合成器

近幾年，模糊邏輯已在不同應用領域，有著極為廣泛的運用：例如，網路流量控制、衛星定位系統訊號處理、航空飛行控制、影像處理等，都有運用模糊系統實現其應用之例子。在一個模糊系統中，主要是以歸屬函數特徵化其模糊規則。因此，歸屬函數在模糊推論時，扮演類似大腦思考的角色。所以，歸屬函數的設計，對於模糊系統之建構，極為重要。為了能有效且快速的設計模糊系統，我們必須有一套設計方法可以進行歸屬函數的合成。在本篇論文中，我們針對模糊系統的自動合成器提出了一個有效率而且有系統的設計方法論。藉由一些指定的訓練資料，此方法論依照一些條件重覆地增加新的模糊規則並且使用模擬進化演算法來調整相關的歸屬函數以滿足模糊系統控制效能的需求。此方法論最大的優點是充分利用了梯形歸屬函數的特徵。因此，此方法論能準確且快速地決定新的模糊規則以改善控制的效能。測試效能的資料都顯示出本方法論達到很好的控制效果，而本篇論文最大的貢獻在於大幅縮短了設計模糊系統的時間，因為只需要數個小時的時間便完成一個可靠的模糊系統設計。

關鍵字

模糊系統；歸屬函數；模擬進化演算法；控制誤差；自動合成；模糊邏輯；模糊推論處理器

並列摘要

In the past few years, there have been an increasing number of systems based on fuzzy logic in various fields of applications, such as ATM network control, GPS data processing, aircraft flight control, image processing, and so on. In a fuzzy system, the fuzzy rules are characterized by membership functions. Therefore, when designing a fuzzy system, the membership functions play a very important role because they are the “brain” in the fuzzy inference process. In order to better control the fuzzy system, we need to have an effective design methodology to synthesize the membership functions. In this thesis, we will present an effective and systematic design methodology for automatic synthesis of fuzzy systems, which are characterized by trapezoid-shaped membership functions. Given some training patterns, the proposed design methodology is to satisfy the control performance requirement by iteratively adding new fuzzy rules and tuning their associated membership functions. The main advantage of our approach is that it exploits the inheritances of trapezoid-shaped membership functions. As a result, the new rules can be accurately determined to improve the control performance. The major contribution of our work is that it greatly shortens the design time of fuzzy systems. Benchmark data consistently shows that the proposed approach may automatically synthesize a reliable fuzzy system within only several hours.

並列關鍵字

Fuzzy System ； Membership functions ； Simulated Evolution Algorithm ； Control Error ； Automatic Synthesis ； and Fuzzy Inference Processor

參考文獻

[1] S.H. Huang and J.Y. Lai, “A High-Speed VLSI Fuzzy Logic Controller with Pipeline Architecture”, in the Proc. of IEEE International Conference on Fuzzy Systems, Vol. 3, pp. 8-11, 2001.

[2] C. W. Tao and J. S. Taur, “Design of Fuzzy-Learning Fuzzy Controllers”, in the Proc. of IEEE International Conference on Fuzzy Systems, Vol. 1, pp. 416-421, 1998.

[3] G. Liu and W. Yang, “Learning and Tuning of Fuzzy Membership Functions by Simulated Annealing Algorithm”, in the Proc. of IEEE Asia Pacific Conference on Circuits and Systems, pp. 367-370, 2000.

[4] B.D. Liu, C.Y. Chen and J.Y. Tsao, “Design of Adaptive Fuzzy Logic Controller Based on Linguistic-Hedge Concepts and Genetic Algorithms”, IEEE Trans. on System, Man and Cybernetics, Vol. 31, No. 1, pp. 32-53, 2001.

[5] J.M. Jou, P.Y. Chen and S.F. Yang, “An Adaptive Fuzzy Logic Controller: It’s VLSI Architecture and Application”, IEEE Trans. on VLSI Systems, Vol. 8, No. 1, 2000.

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梯形歸屬函數之模糊系統的自動合成器

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