Accuracy Improvements to Find the Balance Interpretability-Accuracy in Linguistic Fuzzy Modeling: An Overview

System modeling with fuzzy rule-based systems (FRBSs), i.e. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to faithfully represent the real system. While linguistic FM (mainly developed by linguistic FRBSs) is focused on the interpretability, precise FM (mainly developed by Takagi-Sugeno-Kang FRBSs) is focused on the accuracy. Since both criteria are of vital importance in system modeling, the balance between them has started to pay attention in the fuzzy community in the last few years.

[1]  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..

[2]  George Lakoff,et al.  Hedges: A study in meaning criteria and the logic of fuzzy concepts , 1973, J. Philos. Log..

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

[4]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[5]  Charles L. Karr,et al.  Genetic algorithms for fuzzy controllers , 1991 .

[6]  Philip R. Thrift,et al.  Fuzzy Logic Synthesis with Genetic Algorithms , 1991, ICGA.

[7]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[8]  Bernadette Bouchon-Meunier,et al.  Linguistic modifiers and imprecise categories , 1992, Int. J. Intell. Syst..

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

[10]  H. Surmann,et al.  Self-Organizing and Genetic Algorithms for an Automatic Design of Fuzzy Control and Decision Systems , 1993 .

[11]  F. Klawonn.  Fuzzy sets and vague environments , 1994 .

[12]  Andreas Bastian,et al.  How to Handle the Flexibility of Linguistic Variables with Applications , 1994, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[13]  Antony Satyadas,et al.  GA-optimized fuzzy controller for spacecraft attitude control , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[14]  J. Liska,et al.  Complete design of fuzzy logic systems using genetic algorithms , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[15]  Z. Zenn Bien,et al.  Design of Fuzzy Logic Controller with Inconsistent Rule Base , 1994, J. Intell. Fuzzy Syst..

[16]  Abdollah Homaifar,et al.  Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[17]  Torsten Bohlin.  Special issue on grey box modelling , 1995 .

[18]  Hisao Ishibuchi,et al.  Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[19]  Francisco Herrera,et al.  Tuning fuzzy logic controllers by genetic algorithms , 1995, Int. J. Approx. Reason..

[20]  Yufei Yuan,et al.  A genetic algorithm for generating fuzzy classification rules , 1996, Fuzzy Sets Syst..

[21]  Ahmad Lotfi,et al.  Interpretation preservation of adaptive fuzzy inference systems , 1996, Int. J. Approx. Reason..

[22]  Alistair Munro,et al.  Evolving fuzzy rule based controllers using genetic algorithms , 1996, Fuzzy Sets Syst..

[23]  Hong Yan,et al.  Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition , 1996, Advances in Fuzzy Systems - Applications and Theory.

[24]  Jacques Periaux,et al.  Genetic Algorithms in Engineering and Computer Science , 1996 .

[25]  Francisco Herrera,et al.  Genetic Algorithms and Soft Computing , 1996 .

[26]  Piero P. Bonissone,et al.  Genetic algorithms for automated tuning of fuzzy controllers: a transportation application , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[27]  Dimiter Driankov,et al.  Fuzzy Model Identification , 1997, Springer Berlin Heidelberg.

[28]  P. Lindskog.  Fuzzy identification from a grey box modeling point of view , 1997 .

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

[30]  Francisco Herrera,et al.  A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples , 1997, Int. J. Approx. Reason..

[31]  Luis Magdalena,et al.  A Fuzzy logic controller with learning through the evolution of its knowledge base , 1997, Int. J. Approx. Reason..

[32]  Witold Pedrycz,et al.  Nonlinear context adaptation in the calibration of fuzzy sets , 1997, Fuzzy Sets Syst..

[33]  Luis Magdalena,et al.  Adapting the gain of an FLC with genetic algorithms , 1997, Int. J. Approx. Reason..

[34]  Francisco Herrera,et al.  Applicability of the fuzzy operators in the design of fuzzy logic controllers , 1997, Fuzzy Sets Syst..

[35]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[36]  Ricardo Ribeiro Gudwin,et al.  Context Adaptation in Fuzzy Processing , 1998 .

[37]  Tzung-Pei Hong,et al.  Integrating fuzzy knowledge by genetic algorithms , 1998, IEEE Trans. Evol. Comput..

[38]  María José del Jesús,et al.  Genetic learning of fuzzy rule-based classification systems cooperating with fuzzy reasoning methods , 1998, Int. J. Intell. Syst..

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

[40]  Witold Pedrycz,et al.  Context adaptation in fuzzy processing and genetic algorithms , 1998, Int. J. Intell. Syst..

[41]  Antonio González Muñoz,et al.  A fuzzy theory refinement algorithm , 1998, Int. J. Approx. Reason..

[42]  Kuhu Pal,et al.  Handling of inconsistent rules with an extended model of fuzzy reasoning , 1999, J. Intell. Fuzzy Syst..

[43]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[44]  Antonio González Muñoz,et al.  A Study About the Inclusion of Linguistic Hedges in a Fuzzy Rule Learning Algorithm , 1999, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[45]  Antonio F. Gómez-Skarmeta,et al.  Fuzzy modeling with hybrid systems , 1999, Fuzzy Sets Syst..

[46]  Rudolf Kruse,et al.  Neuro-fuzzy systems for function approximation , 1999, Fuzzy Sets Syst..

[47]  Bernhard Sendhoff,et al.  On generating FC3 fuzzy rule systems from data using evolution strategies , 1999, IEEE Trans. Syst. Man Cybern. Part B.

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

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

[50]  Russell C. Eberhart,et al.  Implementation of evolutionary fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..

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

[52]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[53]  Francisco Herrera,et al.  Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule-based systems using simulated annealing , 2000, Int. J. Approx. Reason..

[54]  Dong-Jo Park,et al.  Novel fuzzy logic control based on weighting of partially inconsistent rules using neural network , 2000, J. Intell. Fuzzy Syst..

[55]  Ulrich Bodenhofer,et al.  A Similarity-Based Generalization of Fuzzy Orderings Preserving the Classical Axioms , 2000, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[56]  L. Magdalena,et al.  Genetic Fuzzy C-Means Algorithm for Automatic Generation of Fuzzy Partitions , 2000 .

[57]  Qiang Shen,et al.  From approximative to descriptive models , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[58]  M. Sipper,et al.  Applying Fuzzy CoCo to breast cancer diagnosis , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

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

[60]  Jorge Casillas,et al.  Can Linguistic Modeling Be As Accurate As Fuzzy Modeling Without Losing Its Description To A High Degree , 2000 .

[61]  Francisco Herrera,et al.  A proposal for improving the accuracy of linguistic modeling , 2000, IEEE Trans. Fuzzy Syst..

[62]  Héctor Pomares,et al.  Self-organized fuzzy system generation from training examples , 2000, IEEE Trans. Fuzzy Syst..

[63]  L. Litz,et al.  Fuzzy modeling based on premise optimization , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[64]  Richard Lai,et al.  Constraining the optimization of a fuzzy logic controller using an enhanced genetic algorithm , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[65]  Héctor Pomares,et al.  A systematic approach to a self-generating fuzzy rule-table for function approximation , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[66]  Francisco Herrera,et al.  A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base , 2001, Inf. Sci..

[67]  Bin-Da Liu,et al.  Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms , 2001, IEEE Trans. Syst. Man Cybern. Part B.

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

[69]  Robert Babuška,et al.  A multi-objective evolutionary algorithm for fuzzy modeling , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[70]  Francisco Herrera,et al.  Linguistic modeling with weighted double-consequent fuzzy rules based on cooperative coevolution , 2003, EUSFLAT Conf..

[71]  Francisco Herrera,et al.  Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base , 2001, IEEE Trans. Fuzzy Syst..

[72]  Witold Pedrycz,et al.  Fuzzy equalization in the construction of fuzzy sets , 2001, Fuzzy Sets Syst..

[73]  Takeshi Furuhashi,et al.  Fuzzy modeling using genetic algorithms with fuzzy entropy as conciseness measure , 2001, Inf. Sci..

[74]  Moshe Sipper,et al.  Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling , 2001, IEEE Trans. Fuzzy Syst..

[75]  Francisco Herrera,et al.  Cooperative Coevolution for Learning Fuzzy Rule-Based Systems , 2001, Artificial Evolution.

[76]  M. J. del Jesus,et al.  Genetic tuning of fuzzy rule-based systems integrating linguistic hedges , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[77]  María José del Jesús,et al.  Some relationships between fuzzy and random set-based classifiers and models , 2002, Int. J. Approx. Reason..

[78]  José Manuel Benítez,et al.  Interpretation of artificial neural networks by means of fuzzy rules , 2002, IEEE Trans. Neural Networks.

[79]  Francisco Herrera,et al.  Linguistic modeling by hierarchical systems of linguistic rules , 2002, IEEE Trans. Fuzzy Syst..

[80]  Francisco Herrera,et al.  COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[81]  Marcelo Simoes.  Introduction to Fuzzy Control , 2003 .

[82]  Brigitte Charnomordic,et al.  Generating an interpretable family of fuzzy partitions from data , 2004, IEEE Transactions on Fuzzy Systems.