Design of Transparent Mamdani Fuzzy Inference Systems

In this paper, we propose a technique to design Fuzzy Inference Systems (FIS) of Mamdani type with transparency constraints. The technique is based on our Crisp Double Clustering algorithm, which is able to discover transparent fuzzy relations that can be directly translated into a human understandable rule base. As a key feature, the user can tune the granularity level of the rule base so as to properly balance the trade off between accuracy and transparency. The resulting FIS bears a transparent knowledge base that can be easily understood by human users and can be effectively used to solve soft computing problems. The work is accompanied by an illustrative example that show the validity of the approach.

[1]  Robert Babuska,et al.  Constructing fuzzy models by product space clustering , 1997 .

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

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

[4]  Rudolf Kruse,et al.  A neuro-fuzzy approach to obtain interpretable fuzzy systems for function approximation , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[5]  J. Casillas.  Interpretability issues in fuzzy modeling , 2003 .

[6]  Rudolf Kruse,et al.  Obtaining interpretable fuzzy classification rules from medical data , 1999, Artif. Intell. Medicine.

[7]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[8]  Giovanna Castellano,et al.  Generation of interpretable fuzzy granules by a double-clustering technique , 2002 .

[9]  Qiang Shen,et al.  From approximative to descriptive fuzzy classifiers , 2002, IEEE Trans. Fuzzy Syst..

[10]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

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

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

[13]  Laurence Tianruo Yang,et al.  Fuzzy Logic with Engineering Applications , 1999 .

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

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

[16]  Jacek M. Leski,et al.  Fuzzy and Neuro-Fuzzy Intelligent Systems , 2000, Studies in Fuzziness and Soft Computing.