Designing Interpretable Fuzzy Systems
暂无分享,去创建一个
Luis Magdalena | Corrado Mencar | Ciro Castiello | Jose Maria Alonso Moral | L. Magdalena | Corrado Mencar | C. Castiello | José María Alonso Moral
[1] F. Herrera,et al. Genetic learning of fuzzy rule‐based classification systems cooperating with fuzzy reasoning methods , 1998 .
[2] Hisao Ishibuchi,et al. Deep Takagi–Sugeno–Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules , 2018, IEEE Transactions on Fuzzy Systems.
[3] Lucien Duckstein,et al. Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems , 1995 .
[4] George C. Mouzouris,et al. Nonsingleton fuzzy logic systems: theory and application , 1997, IEEE Trans. Fuzzy Syst..
[5] Joos Vandewalle,et al. Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm , 2000, IEEE Trans. Fuzzy Syst..
[6] 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..
[7] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[8] Lotfi A. Zadeh,et al. The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..
[9] Takeshi Furuhashi,et al. Fuzzy modeling using genetic algorithms with fuzzy entropy as conciseness measure , 2001, Inf. Sci..
[10] Andri Riid,et al. Identification of transparent, compact, accurate and reliable linguistic fuzzy models , 2011, Inf. Sci..
[11] W. Pedrycz,et al. An introduction to fuzzy sets : analysis and design , 1998 .
[12] Luis Magdalena,et al. Interpretability Constraints and Criteria for Fuzzy Systems , 2021 .
[13] Yaochu Jin,et al. Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement , 2000, IEEE Trans. Fuzzy Syst..
[14] 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.
[15] 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).
[16] Hisao Ishibuchi,et al. Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..
[17] José M. Alonso,et al. HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers , 2011, Soft Comput..
[18] Francisco Herrera,et al. A proposal for improving the accuracy of linguistic modeling , 2000, IEEE Trans. Fuzzy Syst..
[19] Hisao Ishibuchi,et al. Rule weight specification in fuzzy rule-based classification systems , 2005, IEEE Transactions on Fuzzy Systems.
[20] Francisco Herrera,et al. Applicability of the fuzzy operators in the design of fuzzy logic controllers , 1997, Fuzzy Sets Syst..
[21] M. Sugeno,et al. Structure identification of fuzzy model , 1988 .
[22] J. Liska,et al. Complete design of fuzzy logic systems using genetic algorithms , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.
[23] Andreas Bastian,et al. How to Handle the Flexibility of Linguistic Variables with Applications , 1994, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[24] Jesús Alcalá-Fdez,et al. A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning , 2011, IEEE Transactions on Fuzzy Systems.
[25] Francisco Herrera,et al. A learning process for fuzzy control rules using genetic algorithms , 1998, Fuzzy Sets Syst..
[26] Hani Hagras,et al. Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification , 2017, IEEE Transactions on Fuzzy Systems.
[27] 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.
[28] Ahmad Lotfi,et al. Interpretation preservation of adaptive fuzzy inference systems , 1996, Int. J. Approx. Reason..
[29] Francisco Herrera,et al. On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection , 2008, Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases.
[30] Jose Jesus Castro-Schez,et al. Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems , 1999, Fuzzy Sets Syst..
[31] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[32] Tzung-Pei Hong,et al. Effect of merging order on performance of fuzzy induction , 1999, Intell. Data Anal..
[33] Magne Setnes,et al. Compact and transparent fuzzy models and classifiers through iterative complexity reduction , 2001, IEEE Trans. Fuzzy Syst..
[34] Krzysztof Cpałka,et al. Improving Fuzzy Systems Interpretability by Appropriate Selection of Their Structure , 2017 .
[35] José M. Alonso,et al. Highly Interpretable Linguistic Knowledge Bases Optimization: Genetic Tuning versus Solis-Wetts. Looking for a good interpretability-accuracy trade-off , 2007, 2007 IEEE International Fuzzy Systems Conference.
[36] Dr. Hans Hellendoorn,et al. An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.
[37] Torsten Bohlin. Special issue on grey box modelling , 1995 .
[38] W. Pedrycz,et al. Construction of fuzzy models through clustering techniques , 1993 .
[39] Anil K. Jain,et al. Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[40] José M. Alonso,et al. Embedding HILK in a three-objective evolutionary algorithm with the aim of modeling highly interpretable fuzzy rule-based classifiers , 2010, 2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS).
[41] Bernhard Sendhoff,et al. On generating FC3 fuzzy rule systems from data using evolution strategies , 1999, IEEE Trans. Syst. Man Cybern. Part B.
[42] Peter J. Fleming,et al. An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.
[43] Maria J. Fuente,et al. Multi-objective based Fuzzy Rule Based Systems (FRBSs) for trade-off improvement in accuracy and interpretability: A rule relevance point of view , 2017, Knowl. Based Syst..
[44] Moshe Sipper,et al. Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling , 2001, IEEE Trans. Fuzzy Syst..
[45] Luis Magdalena,et al. HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism , 2008 .
[46] Jerry M. Mendel,et al. Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..
[47] Liu Shi. Model Construction Optimization for A Class of Fuzzy Models , 2001 .
[48] Hisao Ishibuchi,et al. A simple but powerful heuristic method for generating fuzzy rules from numerical data , 1997, Fuzzy Sets Syst..
[49] Kenneth A. De Jong,et al. Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.
[50] P. Lindskog. Fuzzy identification from a grey box modeling point of view , 1997 .
[51] Hong Yan,et al. Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition , 1996, Advances in Fuzzy Systems - Applications and Theory.
[52] Héctor Pomares,et al. Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms , 2007, Int. J. Approx. Reason..
[53] L. Zadeh. A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges , 1972 .
[54] José M. Alonso,et al. Interpretability of Fuzzy Systems: Current Research Trends and Prospects , 2015, Handbook of Computational Intelligence.
[55] Jun Zhou,et al. Hierarchical fuzzy control , 1991 .
[56] Chia-Feng Juang,et al. Data-Driven Interval Type-2 Neural Fuzzy System With High Learning Accuracy and Improved Model Interpretability , 2013, IEEE Transactions on Cybernetics.
[57] M. Gupta,et al. Design of fuzzy logic controllers based on generalized T -operators , 1991 .
[58] J. Casillas. Interpretability issues in fuzzy modeling , 2003 .
[59] José M. Alonso,et al. HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism , 2008, Int. J. Intell. Syst..
[60] Luis Magdalena,et al. An Overview of Fuzzy Systems , 2021 .
[61] Michela Antonelli,et al. On the influence of feature selection in fuzzy rule-based regression model generation , 2016, Inf. Sci..
[62] Juan Luis Castro,et al. Balancing Interpretability against Accuracy in Fuzzy Modeling by Means of ACO , 2012, IPMU.
[63] Antonio A. Márquez,et al. A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm , 2012, Int. J. Comput. Intell. Syst..
[64] Francisco Herrera,et al. A 2-tuple fuzzy linguistic representation model for computing with words , 2000, IEEE Trans. Fuzzy Syst..
[65] Ch. Dujet,et al. Force implication: a new approach to human reasoning , 1995 .
[66] Humberto Bustince,et al. CFM-BD: A Distributed Rule Induction Algorithm for Building Compact Fuzzy Models in Big Data Classification Problems , 2019, IEEE Transactions on Fuzzy Systems.
[67] L X Wang,et al. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.
[68] Magne Setnes,et al. Orthogonal transforms for ordering and reduction of fuzzy rules , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).
[69] Antonio Fiordaliso,et al. A constrained Takagi-Sugeno fuzzy system that allows for better interpretation and analysis , 2001, Fuzzy Sets Syst..
[70] Robert P. W. Duin,et al. STATISTICAL PATTERN RECOGNITION , 2005 .
[71] Antonín Dvorák,et al. On linguistic approximation in the frame of fuzzy logic deduction , 1999, Soft Comput..
[72] W. Pedrycz,et al. Context adaptation in fuzzy processing and genetic algorithms , 1998 .
[73] Antonio A. Márquez,et al. Rule base and adaptive fuzzy operators cooperative learning of Mamdani fuzzy systems with multi-objective genetic algorithms , 2009, Evol. Intell..
[74] J. Knapp,et al. Refine and merge: generating small rule bases from training data , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).
[75] M. Amparo Vila,et al. A fuzziness measure for fuzzy numbers: Applications , 1998, Fuzzy Sets Syst..
[76] Ebrahim Mamdani,et al. Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .
[77] Francisco Herrera,et al. Linguistic modeling with weighted double-consequent fuzzy rules based on cooperative coevolution , 2003, EUSFLAT Conf..
[78] José M. Alonso,et al. Multi-objective design of highly interpretable fuzzy rule-based classifiers with semantic cointension , 2011, 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS).
[79] Ronald R. Yager,et al. On the construction of hierarchical fuzzy systems models , 1998, IEEE Trans. Syst. Man Cybern. Part C.
[80] Witold Pedrycz,et al. Fuzzy equalization in the construction of fuzzy sets , 2001, Fuzzy Sets Syst..
[81] Michio Sugeno,et al. A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..
[82] Isao Hayashi,et al. NN-driven fuzzy reasoning , 1991, Int. J. Approx. Reason..
[83] 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..
[84] 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).
[85] Ulrich Bodenhofer,et al. Towards an Axiomatic Treatment of "Interpretability" , 2000 .
[86] Myriam Regattieri Delgado,et al. Towards Interpretable General Type-2 Fuzzy Classifiers , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.
[87] Anna Maria Fanelli,et al. Design of Strong Fuzzy Partitions from Cuts , 2013, EUSFLAT Conf..
[88] Beatrice Lazzerini,et al. A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems , 2007, Soft Comput..
[89] C. Ibbs,et al. The effects of membership function on fuzzy reasoning , 1991 .
[90] Russell C. Eberhart,et al. Implementation of evolutionary fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..
[91] Francisco Herrera,et al. A Multiobjective Evolutionary Algorithm for Tuning Fuzzy Rule Based Systems with Measures for Preserving Interpretability , 2009, IFSA/EUSFLAT Conf..
[92] Luis Magdalena,et al. Semantic interpretability in hierarchical fuzzy systems: Creating semantically decouplable hierarchies , 2019, Inf. Sci..
[93] Antonio González Muñoz,et al. A fuzzy theory refinement algorithm , 1998, Int. J. Approx. Reason..
[94] Christian Wagner,et al. Interpretability indices for hierarchical fuzzy systems , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[95] Beatrice Lazzerini,et al. Multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems , 2009, Evol. Intell..
[96] Pietro Ducange,et al. Optimizing Partition Granularity, Membership Function Parameters, and Rule Bases of Fuzzy Classifiers for Big Data by a Multi-objective Evolutionary Approach , 2019, Cognitive Computation.
[97] Chuen-Chien Lee,et al. Fuzzy logic in control systems: fuzzy logic controller. II , 1990, IEEE Trans. Syst. Man Cybern..
[98] Cynthia Rudin,et al. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.
[99] Henk B. Verbruggen,et al. Complexity reduction in fuzzy modeling , 1998 .
[100] Uzay Kaymak,et al. Similarity measures in fuzzy rule base simplification , 1998, IEEE Trans. Syst. Man Cybern. Part B.
[101] Antonio Fiordaliso. Autostructuration of fuzzy systems by rules sensitivity analysis , 2001, Fuzzy Sets Syst..
[102] Héctor Pomares,et al. Self-organized fuzzy system generation from training examples , 2000, IEEE Trans. Fuzzy Syst..
[103] Kuhu Pal,et al. Handling of inconsistent rules with an extended model of fuzzy reasoning , 1999, J. Intell. Fuzzy Syst..
[104] F. Herrera,et al. Accuracy Improvements in Linguistic Fuzzy Modeling , 2003 .
[105] Luis Magdalena,et al. Revisiting Indexes for Assessing Interpretability of Fuzzy Systems , 2021 .
[106] María José del Jesús,et al. Some relationships between fuzzy and random set-based classifiers and models , 2002, Int. J. Approx. Reason..
[107] Christian Wagner,et al. Toward a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems—A Participatory Design Approach , 2020, IEEE Transactions on Fuzzy Systems.
[108] Francisco Herrera,et al. Tuning fuzzy logic controllers by genetic algorithms , 1995, Int. J. Approx. Reason..
[109] Francisco Herrera,et al. Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.
[110] Anna Maria Fanelli,et al. Interpretable fuzzy partitioning of classified data with variable granularity , 2019, Appl. Soft Comput..
[111] Brigitte Charnomordic,et al. Generating an interpretable family of fuzzy partitions from data , 2004, IEEE Transactions on Fuzzy Systems.
[112] Luis Magdalena. Do Hierarchical Fuzzy Systems Really Improve Interpretability? , 2018, IPMU.
[113] Michio Sugeno,et al. Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.
[114] Francisco Herrera,et al. Linguistic modeling by hierarchical systems of linguistic rules , 2002, IEEE Trans. Fuzzy Syst..
[115] Raúl Pérez,et al. Completeness and consistency conditions for learning fuzzy rules , 1998, Fuzzy Sets Syst..
[116] Derek A. Linkens,et al. Input selection and partition validation for fuzzy modelling using neural network , 1999, Fuzzy Sets Syst..
[117] José M. Alonso,et al. A Conceptual Framework for Understanding a Fuzzy System , 2009, IFSA/EUSFLAT Conf..
[118] Juan Luis Castro,et al. Learning maximal structure fuzzy rules with exceptions , 2001, EUSFLAT Conf..
[119] Witold Pedrycz. Fuzzy Modelling: Paradigms and Practice , 2011 .
[120] Francisco Herrera,et al. Cooperative Coevolution for Learning Fuzzy Rule-Based Systems , 2001, Artificial Evolution.
[121] Francisco Herrera,et al. Building fuzzy graphs: Features and taxonomy of learning for non-grid-oriented fuzzy rule-based systems , 2001, J. Intell. Fuzzy Syst..
[122] Luis Magdalena,et al. Adapting the gain of an FLC with genetic algorithms , 1997, Int. J. Approx. Reason..
[123] H. Hellendoorn,et al. Defuzzification in Fuzzy Controllers , 1993, J. Intell. Fuzzy Syst..
[124] Luis Magdalena,et al. On the role of context in hierarchical fuzzy controllers , 2002, Int. J. Intell. Syst..
[125] J. Yen,et al. An SVD-based fuzzy model reduction strategy , 1996, Proceedings of IEEE 5th International Fuzzy Systems.
[126] J. B. Kiszka,et al. The influence of some fuzzy implication operators on the accuracy of a fuzzy model-part II , 1985 .
[127] John Yen,et al. Improving the interpretability of TSK fuzzy models by combining global learning and local learning , 1998, IEEE Trans. Fuzzy Syst..
[128] Marwan Bikdash,et al. A highly interpretable form of Sugeno inference systems , 1999, IEEE Trans. Fuzzy Syst..
[129] Beatrice Lazzerini,et al. Learning knowledge bases of multi-objective evolutionary fuzzy systems by simultaneously optimizing accuracy, complexity and partition integrity , 2011, Soft Comput..
[130] 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..
[131] Rudolf Kruse,et al. How the learning of rule weights affects the interpretability of fuzzy systems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).
[132] Xing Zong-Yi,et al. Multi-objective fuzzy modeling using NSGA-II , 2008, 2008 IEEE Conference on Cybernetics and Intelligent Systems.
[133] José Valente de Oliveira,et al. Towards neuro-linguistic modeling: Constraints for optimization of membership functions , 1999, Fuzzy Sets Syst..
[134] Luis Magdalena,et al. A Fuzzy logic controller with learning through the evolution of its knowledge base , 1997, Int. J. Approx. Reason..
[135] Charles L. Karr,et al. Genetic algorithms for fuzzy controllers , 1991 .
[136] Bernadette Bouchon-Meunier,et al. Linguistic modifiers and imprecise categories , 1992, Int. J. Intell. Syst..
[137] 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..
[138] Hisao Ishibuchi,et al. Three-objective genetics-based machine learning for linguistic rule extraction , 2001, Inf. Sci..