跳到主要內容

臺灣博碩士論文加值系統

(44.200.171.156) 您好!臺灣時間:2023/03/22 02:56
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:朱修明
論文名稱:模糊模型規則庫自動建立之演算法
論文名稱(外文):An improved approach to automatically build fuzzy model rules
指導教授:王乃堅楊宗銘楊宗銘引用關係
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
中文關鍵詞:模糊理論系統識別最陡坡降法
相關次數:
  • 被引用被引用:3
  • 點閱點閱:392
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
本論文是以模糊理論為基礎建立一套演算法應用於模糊模型規則庫自動建立方面的問題,利用MATLAB程式語言進行電腦模擬,並以數個範例來進行比較和証明此演算法的正確性。
論文中提出的方法是先將未知系統的輸出資料做模糊分類,使用FCM (Fuzzy C-means) 演算法計算出每一類的中心向量,再利用SC準則來決定出合適的分類群數。此分類群數便用來當作模糊規則庫中的規則數目。此規則數目將未知系統分成數個線性系統,使用FCRM (Fuzzy C-Regression Model) 演算法找出輸入/輸出資料對與這些線性系統的模糊關係,以歸屬值矩陣表示之,並求出初步的模糊規則參數值。最後再以最陡坡降法同時對前件部和後件部參數做最佳化調整,直到找出一組參數值能使得該模糊系統達到設定條件的要求。
本文提出之方法架構精簡且相較於其他方法具有較佳的彈性及效果,可做為將來研究模糊模型規則庫自動建立之基礎。

摘 要I
ABSTRACTII
誌 謝III
圖 索 引IV
表 索 引V
目 錄VI
第1章緒論1
1.1研究動機1
1.2研究目的及方法2
1.3內容大綱3
第2章系統識別的介紹4
2.1模糊模型的系統識別4
2.2以模糊理論方法處理系統識別問題8
第3章模糊理論介紹11
3.1傳統集合12
3.2模糊集合13
3.3模糊邏輯15
3.4模糊推論16
3.5各種模糊模型(Fuzzy Models)的比較18
3.5.1模糊系統架構19
3.5.2Takagi and Sugeno’s Fuzzy Model21
3.5.3Sugeno and Yasukawa’s Fuzzy Model22
第4章研究方法25
4.1決定線性系統數目26
4.1.1FCM演算法27
4.1.2SC準則30
4.2建立初步的模糊系統參數值31
4.2.1FCRM演算法31
4.2.2前件部參數建構方法35
4.2.3後件部參數建構方法40
4.3模糊系統參數的最佳化調整40
4.3.1最陡坡降法42
4.3.2建立目標函數與尋找最佳化係數44
4.4設定停止條件48
第5章實驗結果與比較49
5.1範例一、兩個輸入變數之非線性函數建模49
5.2範例二、兩個輸入變數之Sinc函數建模53
5.3範例三、三個輸入變數之非線性函數建模57
第6章結論62
參 考 文 獻64
作 者 簡 介68

[1]J. —S. R. Jang, C. -T. Sun and E. Mizutani, Neuro-fuzzy and soft computing, Prentice-Hall, pp. 95-97, 1997.
[2]L. A. Zadeh, “Fuzzy sets,” Inform. Control, vol. 8, pp. 335-353, 1965.
[3]_, “Editorial: Fuzzy models-What are they and why?,” IEEE Transactions on Fuzzy Systems, vol. 1, pp. 1-6, Feb. 1993.
[4]T. Takigi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. Syst., Man, Cybern., vol. SMC-15, pp. 116-132, Jan./Feb. 1985.
[5]J. —S. R. Jang, “Fuzzy controllers based on temporal back propagation,” IEEE Trans. Neural Networks NN-3, pp. 714-723, 1992.
[6]C. T. Sun, “Rule-based structure identification in an adaptive network based fuzzy inference system,” IEEE Transactions on Fuzzy Systems FS-3, pp. 64-73, 1994.
[7]Y. Nakamori and M. Ryoke, “Identification of fuzzy prediction models through hyper ellipsoidal clustering,” IEEE Trans. System Nan Cybernet. SMC-24, pp. 1153-1173, 1994.
[8]E. Kim, M. Park, S. Ji and M. Park, “A new approach to fuzzy modeling,” IEEE Transactions on Fuzzy Systems, vol. 5,no. 3, pp. 328-337, Aug. 1997.
[9]J. C. Bezdek, Pattern Recognition with Fuzzy Objective Functional Algorithm. New York: Plenum,1981.
[10]M. Sugeno and T. Yasukawa, “A fuzzy-logic-based approach to qualitative modeling,” IEEE Transactions on Fuzzy Systems, vol. 1, pp. 7-31, Feb. 1993.
[11]R. Hathaway and J. C. Bezdek, “Switching regression model and fuzzy clustering,” IEEE Transactions on Fuzzy Systems, vol. 1, pp. 195-204, Aug. 1993.
[12]S. Haykin, Neural networks : a comprehensive foundation, New York : Maxwell Macmillan International, 1994.
[13]M. Sugeno and G. T. Kang, “Fuzzy modeling and control of multilayer incinerator,” Fuzzy Sets and Systems, vol. 18, pp. 329-346, 1986.
[14]J. Q. Chen and L. J. Chen, “An on-line identification algorithm for fuzzy systems,” Fuzzy Sets and Systems, vol. 64, pp. 63-72, 1994.
[15]W. Pedrycz, ”Identification algorithm in fuzzy relational systems,” Fuzzy Sets and Systems, vol. 13, pp. 153-167, 1984.
[16]X. W. Xu and Z. Y. Lu, ”Fuzzy model identification and self-learning for dynamic systems,” IEEE Trans. System Man Cybernet. SMC-17, pp. 683-689, 1987.
[17]C. T. Lin and C. S. G. Lee, “Neural network based fuzzy logic control and decision system,” IEEE Trans. Comput. C-40, pp.1320-1336, 1991.
[18]R. M. Tong, “The evaluation of fuzzy models derived from experimental data,” Fuzzy Sets and Systems, vol. 4, pp. 1-12, 1980.
[19]S. Horikawa, T. Furuhashi and Y. Uchikawa, “On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm,” IEEE Trans. Neural Networks, vol. 3, pp. 801-806, May 1992.
[20]Y. Lin and G. A. Cunningham III, “A new approach to fuzzy-neural modeling,” IEEE Transactions on Fuzzy Systems, vol. 3, pp. 190-197, May 1995.
[21]C. W. Xu and Y. Z. Lu, “Fuzzy model identification and self-learning for dynamic systems,” IEEE Trans. Syst., Man, Cybern., vol. SMC-17, pp. 683-689, July/Aug. 1987.
[22]J. Q. Chen, Y. G. Xi and Z. J. Zhang, “A clustering algorithm for fuzzy model identification,” Fuzzy Sets and Systems, vol. 98, pp. 319-329, 1998.
[23]L. X. Wang and J. M. Mendel, “Fuzzy basis functions, universal approximation, and orthogonal least-squares learning,” IEEE Trans. Neural Networks NN-3, pp. 807-814, 1992.
[24]L. —X. Wang, Adaptive fuzzy systems and control: design and stability analysis. Englewood Cliffs, NJ: Prentice-Hall, 1994.
[25]S. —K. Sin and R. J. P. de Figueiredo, “Fuzzy system design through fuzzy clustering and optimal predefuzzification,” in 2nd IEEE Int. Conf. Fuzzy Syst., San Francisco, CA, pp. 190-195, Mar. 1993.
[26]M. Sugeno and G. T. Kang, “Structure identification of fuzzy model,” Fuzzy Sets and Systems, vol. 28, pp. 15-33, 1988.
[27]M. Sugeno and K. Tanaka, “Structure identification of a fuzzy model and its applications to prediction of a complex system,” Fuzzy Sets and Systems, vol. 42, pp. 315-334, 1991.
[28]L. Wang and P. Langari, “Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques,” IEEE Fuzzy Syst., vol. 3, pp. 454-458, Nov. 1995.
[29]J. C. Bezdek, Fuzzy Mathematics in Pattern Classification. PhD thesis, Applied Math. Center, Cornell University, Ithaca, 1973.
[30]J. C. Bezdek and J. C. Dunn, “Optimal fuzzy partitions: a heuristic for estimating the parameters of a mixture of normal distributions,” IEEE Trans. Comput., vol. 24, pp. 835-838, 1975.
[31]J. C. Bezdek, R. J. Hathaway, R. E. Howard, C.A. Wilson and M. P. Windham, “Local convergence analysis of a grouped variable version of coordinate descent,” Journal of Optimization Theory and Applications, vol. 54, no. 3, pp. 471-477, 1987.
[32]R. J. Hathaway and J. C. Bezdek, “Grouped corrdinate minimization using Newton’s method for inexact minimization in one vector coordinate,” Journal of Optimization Theory and Applications, vol. 71, no. 3, pp. 503-516, 1991.
[33]S. E. Fahlman, “Faster-learning variations on back-propagation: an empirical study. In D. Touretzky, G. Hinton, and T. Sejnowski, editors,” Proceedings of the 1988 Connectionist Models Summer School, pp. 38-51, Carnegic Mellon University, 1988.
[34]T. Kondo, “Revised GMDH algorithm estimating degree of the complete polynomial,” Tran. Of the Society of Instrument and Control Engineers, vol. 22(9), pp. 928-934, 1986. (Japanese).
[35]H. Takagi and I. Hayashi, “NN-driven fuzzy reasoning,” International Journal of Approximate Reasoning, vol. 5(3), pp. 191-212, 1991.
[36]I. H. Suh, J. -H. Kim and F. C. -H. Rhee, “Convex-set-based fuzzy clustering,” IEEE Transactions on Fuzzy Systems, vol. 7, no. 3, pp. 271-285, Jun. 1999.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top