Towards true linguistic modelling through optimal numerical solutions

This paper is concerned with both the problems of quantitative and qualitative modelling of complex systems by using fuzzy techniques. A unified approach for the identification and subsequent extraction of linguistic knowledge of systems using fuzzy relational models is addressed. This approach deals with the identification problem by means of optimal numerical solutions based on weighted least squares and quadratic programming formulations. The linguistic knowledge is extracted in the form of consistent fuzzy rules that describe linguistically the behaviour of the identified system. A new methodology for the simplification of the extracted rules is derived by using a pruning criterion based on the representability matrix concept introduced in previous work. Several numerical aspects concerning the proposed optimization schemes and a covering discussion about the linguistic interpretation of the resulting models are also included together with illustrative examples in the contexts of pattern classification and dynamic systems identification. The paper also provides an overview of fuzzy modelling techniques that intends to situate the relational models among other fuzzy model architectures typically adopted in the literature, highlighting their main advantages and drawbacks.

[1]  Li-Xin Wang,et al.  A note on universal approximation by hierarchical fuzzy systems , 2000, Inf. Sci..

[2]  W. Pedrycz Processing in relational structures: fuzzy relational equations , 1991 .

[3]  Dimitar Filev Fuzzy modeling of complex systems , 1991, Int. J. Approx. Reason..

[4]  José Valente de Oliveira,et al.  Towards neuro-linguistic modeling: Constraints for optimization of membership functions , 1999, Fuzzy Sets Syst..

[5]  Magne Setnes,et al.  Rule-based modeling: precision and transparency , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[6]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[7]  Seok Jong Lee,et al.  CONTROL PROBLEMS IN FUZZY SYSTEMS , 1998 .

[8]  W. Pedrycz Applications of fuzzy relational equations for methods of reasoning in presence of fuzzy data , 1985 .

[9]  W. C. Amaral,et al.  A relational approach for complex system identification , 1999 .

[10]  Robert Shorten,et al.  On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models , 2000, IEEE Trans. Fuzzy Syst..

[11]  Paul C. Rhodes,et al.  Essentials of Fuzzy Modelling and Control , 1995 .

[12]  J. Mendel Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[13]  Bart Kosko,et al.  Fuzzy Engineering , 1996 .

[14]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[15]  Sirish L. Shah,et al.  Fuzzy relational predictive identification , 2000, Fuzzy Sets Syst..

[16]  Hisao Ishibuchi,et al.  Effect of rule weights in fuzzy rule-based classification systems , 2001, IEEE Trans. Fuzzy Syst..

[17]  Yaochu Jin,et al.  Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement , 2000, IEEE Trans. Fuzzy Syst..

[18]  J. Valente de Oliveira,et al.  Towards Neuro-Linguistic Modelling , 1997 .

[19]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[20]  Fernando José Von Zuben,et al.  Evolutionary design of Takagi-Sugeno fuzzy systems: a modular and hierarchical approach , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[21]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[22]  Shiv Dutt Joshi,et al.  A GA-based method for constructing TSK fuzzy rules from numerical data , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[23]  W. Pedrycz,et al.  On identification in fuzzy systems and its applications in control problems , 1981 .

[24]  J. B. Kiszka,et al.  The influence of some fuzzy implication operators on the accuracy of a fuzzy model-part II , 1985 .

[25]  M. Sugeno,et al.  Fuzzy modeling and control of multilayer incinerator , 1986 .

[26]  Magne Setnes,et al.  Compact and transparent fuzzy models and classifiers through iterative complexity reduction , 2001, IEEE Trans. Fuzzy Syst..

[27]  R. Maciel Filho,et al.  Hierarchical neural fuzzy models as a tool for process identification: a bioprocess application , 2001 .

[28]  Yong-Zai Lu,et al.  Fuzzy Model Identification and Self-Learning for Dynamic Systems , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[29]  Witold Pedrycz,et al.  Optimization of fuzzy models , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[30]  D. Willaeys,et al.  THE USE OF FUZZY SETS FOR THE TREATMENT OF FUZZY INFORMATION BY COMPUTER , 1993 .

[31]  Uzay Kaymak,et al.  Similarity measures in fuzzy rule base simplification , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[32]  Paulo J. Costa Branco,et al.  A fuzzy relational identification algorithm and its application to predict the behaviour of a motor drive system , 2000, Fuzzy Sets Syst..

[33]  Witold Pedrycz Identification in fuzzy systems , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[34]  Witold Pedrycz,et al.  An algorithmic framework for development and optimization of fuzzy models , 1996, Fuzzy Sets Syst..

[35]  E. H. Mandami Application of Fuzzy Logic to Approximate Reasoning using Linguistic Synthesis , 1977 .

[36]  Ricardo J. G. B. Campello,et al.  Takagi-Sugeno fuzzy models within orthonormal basis function framework and their application to process control , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[37]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[38]  W. Pedrycz An approach to the analysis of fuzzy systems , 1981 .

[39]  R. M. Tong,et al.  Analysis of fuzzy control algorithms using the relation matrix , 1976 .

[40]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[41]  J. V. Oliveira,et al.  Neuron inspired learning rules for fuzzy relational structures , 1993 .

[42]  H. Zimmermann,et al.  Comparison of fuzzy reasoning methods , 1982 .

[43]  Ricardo J. G. B. Campello,et al.  A Highly Adaptive Algorithm for Fuzzy Modelling of Systems , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[44]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[45]  Korris Fu-Lai Chung,et al.  Analytical resolution and numerical identification of fuzzy relational systems , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[46]  Masaharu Mizumoto,et al.  Product-sum-gravity method=fuzzy singleton-type reasoning method=simplified fuzzy reasoning method , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[47]  Witold Pedrycz,et al.  STRUCTURED FUZZY MODELS , 1985 .

[48]  W. Pedrycz,et al.  Fuzzy relation equations theory as a basis of fuzzy modelling: an overview , 1991 .

[49]  C. Hwang,et al.  A Combined Approach to Fuzzy Model Identification , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[50]  Martin Brown,et al.  Intelligent Control - Aspects of Fuzzy Logic and Neural Nets , 1993, World Scientific Series in Robotics and Intelligent Systems.

[51]  M. Chung,et al.  Identification of fuzzy relational model and its application to control , 1993 .

[52]  G. Klir,et al.  Resolution of finite fuzzy relation equations , 1984 .

[53]  John Yen,et al.  Simplifying fuzzy rule-based models using orthogonal transformation methods , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[54]  Witold Pedrycz,et al.  An Introduction to Fuzzy Sets , 1998 .

[55]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[56]  Magne Setnes,et al.  Supervised fuzzy clustering for rule extraction , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[57]  José Valente de Oliveira,et al.  Semantic constraints for membership function optimization , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[58]  L. Zadeh,et al.  Fuzzy sets and applications : selected papers , 1987 .

[59]  W. Pedrycz Numerical and applicational aspects of fuzzy relational equations , 1983 .

[60]  José Valente de Oliveira,et al.  A design methodology for fuzzy system interfaces , 1995, IEEE Trans. Fuzzy Syst..

[61]  B. Anderson,et al.  Digital control of dynamic systems , 1981, IEEE Transactions on Acoustics, Speech, and Signal Processing.

[62]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[63]  W. Pedrycz,et al.  Algorithms for solving fuzzy relational equations in a probabilistic setting , 1990 .

[64]  Elie Sanchez,et al.  Resolution of Composite Fuzzy Relation Equations , 1976, Inf. Control..

[65]  Robert Babuska,et al.  Rule base reduction: some comments on the use of orthogonal transforms , 2001, IEEE Trans. Syst. Man Cybern. Syst..

[66]  D. Grant Fisher,et al.  Identification algorithms for fuzzy relational matrices, Part 1: Non-optimizing algorithms , 2000, Fuzzy Sets Syst..

[67]  W. Pedrycz An identification algorithm in fuzzy relational systems , 1984 .

[68]  Witold Pedrycz,et al.  A decomposition of fuzzy relations , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[69]  Madan M. Gupta,et al.  On the principles of fuzzy neural networks , 1994 .

[70]  Li-Xin Wang,et al.  Universal approximation by hierarchical fuzzy systems , 1998, Fuzzy Sets Syst..

[71]  W Pedrycz,et al.  Solvability of fuzzy relational equations and manipulation of fuzzy data , 1986 .

[72]  R. Babuska,et al.  Improved inference for Takagi-Sugeno models , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[73]  P.J. King,et al.  The application of fuzzy control systems to industrial processes , 1977, Autom..

[74]  Ronald R. Yager,et al.  Essentials of fuzzy modeling and control , 1994 .

[75]  Joos Vandewalle,et al.  Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm , 2000, IEEE Trans. Fuzzy Syst..

[76]  John Yen,et al.  Improving the interpretability of TSK fuzzy models by combining global learning and local learning , 1998, IEEE Trans. Fuzzy Syst..

[77]  Ching-Chang Wong,et al.  A GA-based method for constructing fuzzy systems directly from numerical data , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[78]  Witold Pedrycz,et al.  When is a fuzzy relation decomposable in two fuzzy sets , 1985 .

[79]  Ricardo J. G. B. Campello,et al.  Optimization of hierarchical neural fuzzy models , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[80]  Witold Pedrycz,et al.  Fuzzy sets engineering , 1995 .

[81]  Robert J. Hammell,et al.  Interpolation, Completion, and Learning Fuzzy Rules , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[82]  João M. Lemos,et al.  Long-range predictive adaptive fuzzy relational control , 1995 .

[83]  Witold Pedrycz,et al.  Distributed fuzzy system modeling , 1995, IEEE Trans. Syst. Man Cybern..

[84]  Marcelo C. M. Teixeira,et al.  Stabilizing controller design for uncertain nonlinear systems using fuzzy models , 1999, IEEE Trans. Fuzzy Syst..

[85]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[86]  Dimiter Driankov,et al.  Fuzzy model identification - selected approaches , 1997 .

[87]  Witold Pedrycz,et al.  Linguistic models and linguistic modeling , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[88]  Chen-Wei Xu,et al.  Fuzzy systems identification , 1989 .

[89]  D. Willaeys,et al.  The use of fuzzy sets for the treatment of fuzzy information by computer , 1981 .

[90]  Roberto Guerrieri,et al.  Fuzzy sets of rules for system identification , 1996, IEEE Trans. Fuzzy Syst..

[91]  W. Pedrycz Approximate solutions of fuzzy relational equations , 1988 .

[92]  J. V. de Oliveira,et al.  On optimal fuzzy systems I/O interfaces , 1993 .

[93]  R. B. Newell,et al.  Fuzzy identification and control of a liquid level rig , 1988 .

[94]  Witold Pedrycz,et al.  Some remarks on the identification problem in fuzzy systems , 1984 .

[95]  W. Pedrycz Why triangular membership functions , 1994 .

[96]  Hisao Ishibuchi,et al.  A simple but powerful heuristic method for generating fuzzy rules from numerical data , 1997, Fuzzy Sets Syst..

[97]  João Miranda Lemos,et al.  Improving adaptive fuzzy control performance by speeding up identification: Application to an electric furnace , 1998, J. Intell. Fuzzy Syst..

[98]  Kevin M. Passino,et al.  Avoiding exponential parameter growth in fuzzy systems , 2001, IEEE Trans. Fuzzy Syst..

[99]  Ronald R. Yager,et al.  On the construction of hierarchical fuzzy systems models , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[100]  Li-Xin Wang,et al.  Analysis and design of hierarchical fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..

[101]  R. B. Newell,et al.  Fuzzy adaptive control of a first-order process , 1989 .

[102]  L. Zadeh Fuzzy sets as a basis for a theory of possibility , 1999 .

[103]  M. B. Zarrop,et al.  Self-Tuning Systems: Control and Signal Processing , 1991 .

[104]  W. Pedrycz,et al.  Some design options for optimal fuzzy model I/O interfaces , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[105]  W. Pedrycz,et al.  An introduction to fuzzy sets : analysis and design , 1998 .

[106]  G.H.C. Oliveira,et al.  Fuzzy models within orthonormal basis function framework , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[107]  W. Pedrycz ON GENERALIZED FUZZY RELATIONAL EQUATIONS AND THEIR APPLICATIONS , 1985 .

[108]  Ricardo J. G. B. Campello,et al.  Modeling and linguistic knowledge extraction from systems using fuzzy relational models , 2001, Fuzzy Sets Syst..

[109]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[110]  Witold Pedrycz,et al.  Fuzzy multimodels , 1996, IEEE Trans. Fuzzy Syst..

[111]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[112]  Spyros G. Tzafestas,et al.  Fuzzy relation equations and fuzzy inference systems: an inside approach , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[113]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[114]  George J. Klir,et al.  Identification of fuzzy relation systems , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[115]  Witold Pedrycz,et al.  Fuzzy control and fuzzy systems , 1989 .

[116]  Kazuo Tanaka,et al.  Successive identification of a fuzzy model and its applications to prediction of a complex system , 1991 .

[117]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[118]  R. Tong SYNTHESIS OF FUZZY MODELS FOR INDUSTRIAL PROCESSES-SOME RECENT RESULTS , 1978 .

[119]  D. Grant Fisher,et al.  Identification algorithms for fuzzy relational matrices, Part 2: Optimizing algorithms , 2000, Fuzzy Sets Syst..