臺灣博碩士論文加值系統

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。

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.

摘要
ABSTRACT
目錄
圖目錄
表目錄
第一章 緒論
1-1 研究背景
1-2 研究動機與目的
1-3 研究方法與架構
1-4 全文架構
第二章模糊系統與歸屬函數
2-1 模糊理論歷史演進
2-2 模糊邏輯簡介
2-2-1 歸屬函數的特徵
2-2-2模糊化
2-2-3模糊推論
2-2-4解模糊化
2-3 模糊推論處理器與格式
第三章文獻回顧
第四章梯形歸屬函數之模糊系統的自動合成演算法
4-1 問題定義與規劃
4-2 模糊系統的自動合成演算法
4-3 模糊規則分割演算法
4-4 梯形歸屬函數合成演算法
4-4-1 模擬進化演算法
4-5 模糊規則減化演算法
第五章 實驗結果與討論
5-1 倒車入庫控制系統
5-2 非線性模擬系統
5-3 車載平衡桿系統
5-4 自動合成器實驗
第六章 結論
參考文獻
附錄一
附錄二
附錄三

[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.[6] Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, c1995.[7] M. Togai and H. Watanabe, “Expert System on a Chip: An Engine for Real-Time Approximate Reasoning”, IEEE Expert Magazine, vol. 1, pp. 55-62, 1986.[8] Hanqi Zhuang and Xiaomin Wu, “Membership Function Modification of Fuzzy Logic Controllers with Histogram Equalization”, IEEE Trans. on Cybernetics, vol. 31, pp. 125-132, 2001.[9] D. Van Cleave and K.S. Rattan, “Tuning of Fuzzy Logic Controller using Neural Network”, in the Proc. of IEEE conference on National Aerospace and Electronics, pp. 305-312, 2000.[10] S.H. Huang, W.H. Peng and J.Y. Lai, “A Simulated Evolution Algorithm for The Synthesis of Trapezoid-Shaped Membership Functions”, in the Proc. of National Computer Symposium 2001, pp. B021~B030, 2001.[11] G. Ascia, V. Catania, D. Panno, F. Ficili, and S. Palazzo, “A VLSI Fuzzy Expert System for Real-Time Traffic Control in ATM Networks”, IEEE Trans. on Fuzzy Systems, vol. 5, pp. 20--31, 1997.[12] J. L. Chang, Y. Y. Chen, F. R. Hang, “Fuzzy Processing on GPS Data to Improve the Position Accuracy”, in the Proc. of Intelligent Systems and Information Fuzzy System Symposium, pp. 557--562, 1996.[13] Simutis, R.,” Fuzzy logic based stock trading system”, Proc. of the IEEE/IAFE/INFORMS Conference on Computational Intelligence for Financial Engineering, pp. 19--21, 2000.[14] S. Yasunobu and S. Miyamoto, “Automatic Train Operation by Predictive Fuzzy Control”, in Industrial Application of Fuzzy Control, M. Sugeno, Ed. Amsterdam, The Netherlands: North-Holland, pp. 1--18, 1985.[15] D. Sinha, P. Sinha, E. R. Dougherty and S. Batmen, “Design and Analysis on Fuzzy Morphological Algorithms for Image Processing”, IEEE Trans. on Fuzzy Systems, pp. 570--584, 1997.[16] Barra, V. and Boire, J.Y., “Automatic segmentation of subcortical brain structures in MR images using information fusion”, IEEE Trans. on Medical Imaging, Vol. 20, pp. 549--558, 2001.[17] L. I. Larkin, “A Fuzzy Logic Controller for Aircraft Flight Control”, Industrial Applications of Fuzzy Control, M. Sugeno. Ed. Amsterdam: North Holland, pp. 87--103, 1985.[18] Shouping Guan, Han-Xiong Li and Tso, S.K, “Multivariable Fuzzy Supervisory Control for The Laminar Cooling Process of Hot Rolled Slab”, IEEE Trans. on Control Systems Technology, Vol. 9, pp. 348--356, 2001.[19] Kuang-Yow Lian, Chian-Song Chiu, Tung-Sheng Chiang, and Peter Liu, “LMI-Based Fuzzy Chaotic Synchronization and Communications”, IEEE Trans. on Fuzzy Systems, Vol. 9, No. 4, pp. 539--553, 2001.[20] S.H. Huang, W.H. Peng and J.Y. Lai, “Automatic Synthesis of Fuzzy Systems Based on Trapezoid-shaped Membership Functions”, accepted by IEEE Asia-Pacific Conference on Circuits and Systems, 2002.