以基因演算法為基礎之模糊建模新方法應用於函數近似

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滕有為(You-Wei Teng) 查詢紙本館藏

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電機工程學系

論文名稱

以基因演算法為基礎之模糊建模新方法應用於函數近似
(New GA-Based Fuzzy Modeling Approaches to Function Approximation)

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摘要(中)

本篇論文提出以基因演算法為基礎之模糊建模方法。首先，對於一個未知的系統或是函數，給予此系統一組輸入資料，即可以得到所對應之輸出資料。再針對所收集到之輸入輸出對，將其當作訓練樣本，本篇所提出之演算法可以完善地訓練模糊系統，使其擁有近似未知系統的輸入輸出對應關係。本篇論文的主要考量除了在建立一個系統複雜度較低(使用較少參數/規則數目)、近似效果較好的模糊系統外，並且考慮到使用者的知識背景與建立模糊系統的困難度，而建立一自我組織、全自動化之系統建模方法。此外，對於各輸入變數的重要性與所應分配到的歸屬函數數目，以及不重要甚至是不正確的系統輸入變數的選取與刪除方法也提出了解決方法。各章節中皆與許多其他文獻做比較，並得到令人滿意的實驗結果。

摘要(英)

In this dissertation, the GA-based fuzzy modeling algorithm is proposed. For an unknown system or function, giving a set of input data could generate a corresponding set of output data. The gathered input-output data pairs will be the training data set, and the fuzzy system could be effectively trained by the proposed algorithm to have an approximate input-output relation as the unknown system. This dissertation concerns about not only generating a less-complex (with few parameters/rules) fuzzy system with precise approximation accuracy, but also constructing a self-organized and full-automatic fuzzy modeling method. Moreover, this work has proposed the solutions about determining the significance of each input variable and its membership function number, and even the extraction and rejection methods of less-important or inaccurate input variables. In each chapter, abundant experimental comparisons are presented to prove the effectiveness of this work.

關鍵字(中)

★ 模糊建模
★ 基因演算法
★ 指數型歸屬函數
★ 參數制定

關鍵字(英)

★ exponential membership functions
★ genetic algorithm
★ parameter determination
★ fuzzy modeling

論文目次

中文目錄
摘要一
第一章　緒論三
第二章　使用以區域為基礎之指數歸屬函數模糊建模八
第三章　以基因演算法為基礎之兩階段模糊系統設計九
第四章　以基因演算法為基礎並具使用者親和概念之模糊建模一○
第五章　結論一一
CONTENTS
List of Figures III
List of Tables VI
Abstract VIII
Acronym IX
Chapter 1 Introduction 1
Chapter 2 Fuzzy Modeling with Region-Based Exponential Membership Functions 6
2.1 Introduction 6
2.2 Problem Description 8
2.3 The Algorithm of Fuzzy Modeling 9
2.4 Experimental Results 18
2.5 Summary 35
Chapter 3 Two Stages GA-Based Fuzzy Model Design: Triangular-Partitioned and Exponential-Partitioned Structures 37
3.1 Introduction 37
3.2 Stage 1: The Fuzzy Model with a TP Structure 38
3.3 Stage 2: The Fuzzy Model with a EP Structure 40
3.4 Experimental Results 41
3.5 Summary 47
Chapter 4 GA-Based Fuzzy Modeling with User-Friendly Concepts 48
4.1 Introduction 48
4.2 Problem Formulation and Fuzzy System Structure 50
4.3 Parameter Identification 52
4.3.1 The Antecedent Parameters 52
4.3.2 The Consequent Parameters 57
4.4 Performance Index Examination 58
4.5 Experimental Results 62
4.6 Summary 77
Chapter 5 Conclusions 78
References 80
Publication List 87

參考文獻

[1] L.-X. Wang and J. M. Mendal, “Generating fuzzy rules by learning from examples,” IEEE Trans. Syst., Man, and Cybern., vol. 22, no. 6, pp. 1414–1427, 1992.
[2] M. Sugeno and T. Yasukawa, “A fuzzy-logic-based approach to qualitative modeling,” IEEE Trans. Fuzzy Syst., vol. 1, no. 1, pp. 7–31, 1993.
[3] T. Sudkamp and R. J. Hammell, “Interpolation, completion and learning fuzzy rules,” IEEE Trans. Syst., Man., Cybern., vol. 24, no. 2, pp. 332–342, 1994.
[4] E. Kim, M. Park, S. Ji, and M. Park, “A new approach to fuzzy modeling,” IEEE Trans. Fuzzy Syst., vol. 5, no. 3, pp. 328–337, 1997.
[5] T.-P. Wu and S.-M. Chen, “A new method for constructing membership functions and fuzzy rules from training examples,” IEEE Trans. Syst., Man, and Cybern. B, vol. 29, no. 1, pp. 25–40, 1999.
[6] C.-C. Wong and C.-C. Chen, “A hybrid clustering and gradient descent approach for fuzzy modeling,” IEEE Trans. Syst., Man, and Cybern. B, vol. 29, no. 6, pp. 686–693, 1999.
[7] C.-C. Wong and C.-C. Chen, “A GA-based method for constructing fuzzy systems directly from numerical data,” IEEE Trans. Syst., Man, Cybern. B, vol. 30, no. 6, pp. 904–911, 2000.
[8] M. Setnes and H. Roubos, “GA-fuzzy modeling and classification: complexity and performance,” IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 509–522, 2000.
[9] M. Russo, “FuGeNeSys—A fuzzy genetic neural system for fuzzy modeling,” IEEE Trans. Fuzzy Syst., vol. 6, no. 3, pp. 373–388, 1998.
[10] H. Roubos and M. Setnes, “Compact and transparent fuzzy models and classifiers through iterative complexity reduction,” IEEE Trans. Fuzzy Syst., vol. 9, no. 4, pp. 516–524, 2001.
[11] Y.-W. Teng and W.-J. Wang, “GA-based fuzzy modeling with an exponential-partitioned structure,” Int. J. Fuzzy Syst., vol. 4, no. 4, pp. 905–910, 2002.
[12] Y.-W. Teng, W.-J. Wang, and C.-H. Chiu, “Function approximation via particular input space partition and region-based exponential membership functions,” Fuzzy Sets Syst., vol. 142, no. 2, pp. 267–291, 2004.
[13] S. Horikawa, T. Furuhashi, and Y. Uchikawa, “On fuzzy modeling using fuzzy neural networks with back-propagation algorithm,” IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 801–806, 1992.
[14] C. M. Higgins and R. M. Goodman, “Fuzzy rule-based networks for control,” IEEE Trans. Fuzzy Syst., vol. 2, no. 1, pp. 82–88, 1994.
[15] S. Abe and M. S. Lan, “Fuzzy rules extraction directly from numerical data for function approximation,” IEEE Trans. Syst., Man, and Cybern., vol. 25, no. 1, pp. 119–129, 1995.
[16] J. S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference systems,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665–685, 1993.
[17] J. J. Buckley, “Sugeno type controllers are universal controllers,” Fuzzy Sets Syst., vol. 53, pp. 209–304, 1993.
[18] B. Kosko, “Fuzzy systems as universal approximators,” IEEE Trans. Comput., vol. 28, no. 1, pp. 1329–1333, 1994.
[19] J. L. Castro and M. Delgado, “Fuzzy systems with defuzzification are universal approximators,” IEEE Trans. Syst., Man, Cybern., vol. 26, no. 1, pp. 149–152, 1996.
[20] I. Rojas, H. Pomares, J. Ortega, and A. Prieto, “Self-organized fuzzy system generation from training examples,” IEEE Trans. Fuzzy Syst., vol. 8, no. 1, pp. 23–36, 2000.
[21] H. Pomares, I. Rojas, J. Gonzalez, and A. Prieto, “Structure identification in complete rule-based fuzzy systems,” IEEE Trans. Fuzzy Syst., vol. 10, no. 3, pp. 349–359, 2002.
[22] C. C. Lee, “Fuzzy logic in control systems: Parts I, II,” IEEE Trans. Syst., Man, Cybern., vol. 20, no. 2, pp. 404–435, 1990.
[23] J. Espinosa and J. Vandewalle, “Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm,” IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 591–600, 2000.
[24] O. Cordón, F. Herrera, and P. Villar, “Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base,” IEEE Trans. Fuzzy Syst., vol. 9, no. 4, pp. 667–674, 2001.
[25] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Reading, MA: Addison-Wesley, 1989.
[26] Z. Michalewics, Genetic Algorithms + Data Structures = Evolution Programs. New York: McGraw-Hill, 1994.
[27] J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. Englewood Cliffs, NJ: Prentice Hall, 1997.
[28] H. Pomares, I. Rojas, J. Gonzalez, and A. Prieto, “A systematic approach to a self-generating fuzzy rule-table for function approximation,” IEEE Trans. Syst., Man, Cybern. B, vol. 30, no. 3, pp. 431–447, 2000.
[29] J. H. Nie and T. H. Lee, “Rule-based modeling: fast construction and optimal manipulation,” IEEE Trans. Syst., Man, Cybern. A, vol. 26, no. 6, pp. 728–738, 1996.
[30] W. Pedrycz, “Conditional fuzzy clustering in the design of radial basis function neural networks,” IEEE Trans. Neural Networks, vol. 9, no. 4, pp. 601–612, 1998.
[31] T. A. Runkler and J. C. Bezdek, “Alternating cluster estimation: a new tool for clustering and function approximation,” IEEE Trans. Fuzzy Syst., vol. 7, no. 4, pp. 377–393, 1999.
[32] K. Nozaki, H. Ishibuchi, and H. Tanaka, “A simple but powerful heuristic method for generating fuzzy rules from numerical data,” Fuzzy Sets Syst., vol. 86, pp. 251–270, 1997.
[33] M. Setnes, R. Babuška, U. Kaymak, and H. R. van Nauta Lemke, “Similarity measures in fuzzy rule base simplification,” IEEE Trans. Syst., Man, Cybern. B, vol. 28, no. 3, pp. 376–386, 1998.
[34] F. Jimenez, A. F. Gomez-Skarmeta, H. Roubos, and R. Babuska, “A multi-objective evolutionary algorithm for fuzzy modeling,” in Proc. IFSA World Congress and NAFIPS Int. Conf., Vancouver, Canada, July 2001, pp. 1222–1228.
[35] T. Sudkamp, A. Knapp, and J. Knapp, “Model generation by domain refinement and rule reduction,” IEEE Trans. Syst., Man, Cybern. B, vol. 33, no. 1, pp. 45–55, 2003.
[36] H. Ishibuchi, T. Nakashima, and T. Murata, “Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems,” IEEE Trans. Syst., Man, Cybern. B, vol. 29, no. 5, pp. 601–618, 1999.
[37] A. E. Gaweda and J. M. Zurada, “Data-driven linguistic modeling using relational fuzzy rules,” IEEE Trans. Fuzzy Syst., vol. 11, no. 1, pp. 121–134, 2003.
[38] W.-Y. Wang, T.-T. Lee, C.-L. Liu, and C.-H. Wang, “Function approximation using fuzzy neural networks with robust learning algorithm,”, IEEE Trans. Syst., Man, Cybern., B, vol. 27, no. 4, pp. 740–747, 1997.
[39] R. Thawonmas and S. Abe, “Function approximation based on fuzzy rules extracted from partitioned numerical data,” IEEE Trans. Syst., Man, Cybern., B, vol. 29, no. 4, pp. 525–534, 1999.
[40] C.-C. Chuang, S.-F. Su, and S.-S. Chen, “Robust TSK fuzzy modeling for function approximation with outliers,” IEEE Trans. Fuzzy Syst., vol. 9, no. 6, pp. 810–821, 2001.
[41] I. Batyrshin, O. Kaynak, and I. Rudas, “Fuzzy modeling based on generalized conjunction operations,” IEEE Trans. Fuzzy Syst., vol. 10, no. 5, pp. 678–683, 2002.
[42] H.-H. Tsai and P.-T. Yu, “On the optimal design of fuzzy neural networks with robust learning for function approximation,” IEEE Trans. Syst., Man, Cybern., B, vol. 30, no. 1, pp. 217–223, 2000.
[43] W.-Y. Wang and Y.-H. Li, “Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm,” IEEE Trans. Syst., Man, Cybern., B, vol. 33, no. 6, pp. 966–976, 2003.
[44] J. Gonzalez, H. Rojas, J. Ortega, and A. Prieto, “A new clustering technique for function approximation,” IEEE Trans. Neural Networks, vol. 13, no. 1, pp. 132–142, 2002.
[45] M. Landajo, M.J. Rio, and R. Perez, “A note on smooth approximation capabilities of fuzzy systems,” IEEE Trans. Fuzzy Syst., vol. 9, no. 2, pp. 229–237, 2001.
[46] S.G. Tzafestas and K.C. Zikidis, “NeuroFAST: on-line neuro-fuzzy ART-based structure and parameter learning TSK model,” IEEE Trans. Syst., Man, Cybern., B, vol. 31, no. 5, pp. 797–802, 2001.
[47] L.-X. Wang and W. Chen, “Approximation accuracy of some neuro-fuzzy approaches,” IEEE Trans. Fuzzy Syst., vol. 8, no. 4, pp. 470–478, 2000.
[48] S. Wu and M.J. Er, “Dynamic fuzzy neural networks-a novel approach to function approximation,” IEEE Trans. Syst., Man, Cybern., B, vol. 30, no. 2, pp. 358–364, 2000.
[49] W.-Y. Wang, C.-Y. Cheng, and Y.-G. Leu, “An Online GA-Based Output-Feedback Direct Adaptive Fuzzy-Neural Controller for Uncertain Nonlinear Systems,” IEEE Trans. Syst., Man, Cybern., B, vol. 34, no. 1, pp. 334–345, 2004.
[50] R. Hassine, F. Karray, A.M. Alimi, and M. Selmi, “Approximation properties of fuzzy systems for smooth functions and their first-order derivative,” IEEE Trans. Syst., Man, Cybern., A, vol. 33, no. 2, pp. 160–168, 2003.
[51] D. Tikk, G. Biro, T.D. Gedeon, L.T. Koczy, and J.D. Yang, “Improvements and critique on Sugeno's and Yasukawa's qualitative modeling,” IEEE Trans. Fuzzy Syst., vol. 10, no. 5, pp. 596–606, 2002.
[52] S. Paul and S. Kumar, “Subsethood-product fuzzy neural inference system (SuPFuNIS),” IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 578–599, 2002.
[53] R. Setiono, W.K. Leow, and J.M. Zurada, “Extraction of rules from artificial neural networks for nonlinear regression,“ IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 564–577, 2002.
[54] S. Wu, M.J. Er, and Y. Gao, “A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks,” IEEE Trans. Fuzzy Syst., vol. 9, no. 4, pp. 578–594, 2001.
[55] H. Ishibuchi and T. Nakashima, “Effect of rule weights in fuzzy rule-based classification systems,” IEEE Trans. Fuzzy Syst., vol. 9, no. 4, pp. 506–515, 2001.
[56] J.-C. Duan and F.-L. Chung, “Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning,” IEEE Trans. Fuzzy Syst., vol. 9, no. 2, pp. 293–306, 2001.
[57] M. Delgado, A.F. Gomez-Skarmeta, F. Martin, “A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling,” IEEE Trans. Fuzzy Syst., vol. 5, no. 2, pp. 223–233, 1997.

指導教授

王文俊(Wen-June Wang)

審核日期

2004-7-13

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