Rough-Neuro-Fuzzy System with MICOG Defuzzification

This paper presents a new approach to fuzzy classification in case of missing information about object features. The rough set theory is incorporated into neuro-fuzzy structures and the rough-neuro-fuzzy classifier is derived. The architecture of the classifier is determined by the MICOG (modified indexed center of gravity) defuzzification method. Some illustrative examples are given.

[1]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[2]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control , 1994 .

[3]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[4]  J. Recasens,et al.  Fuzzy Equivalence Relations: Advanced Material , 2000 .

[5]  Leszek Rutkowski,et al.  Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems , 2005, IEEE Transactions on Fuzzy Systems.

[6]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[7]  Robert Nowicki,et al.  Rough Sets in the Neuro-Fuzzy Architectures Based on Non-monotonic Fuzzy Implications , 2004, ICAISC.

[8]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[9]  Leszek Rutkowski,et al.  Neuro-Fuzzy Architectures with Various Implication Operators , 2000 .

[10]  Francesc Esteva,et al.  Review of Triangular norms by E. P. Klement, R. Mesiar and E. Pap. Kluwer Academic Publishers , 2003 .

[11]  Detlef Nauck,et al.  Foundations Of Neuro-Fuzzy Systems , 1997 .

[12]  Jerzy W. Grzymala-Busse,et al.  LERS-A System for Learning from Examples Based on Rough Sets , 1992, Intelligent Decision Support.

[13]  Jordi Recasens,et al.  Fuzzy groups, fuzzy functions and fuzzy equivalence relations , 2004, Fuzzy Sets Syst..

[14]  Keon-Myung Lee,et al.  A fuzzy Neural Network Model for fuzzy Inference and Rule Tuning , 1994, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[15]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[16]  Leszek Rutkowski,et al.  New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing , 2004 .

[17]  D. Rutkowska,et al.  Implication-Based Neuro-Fuzzy Architectures , 2000 .

[18]  J. Recasens,et al.  UPPER AND LOWER APPROXIMATIONS OF FUZZY SETS , 2000 .

[19]  Zdzislaw Pawlak,et al.  Rough sets, decision algorithms and Bayes' theorem , 2002, Eur. J. Oper. Res..

[20]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[21]  Jerzy W. Grzymala-Busse,et al.  An overview of the LERS1 learning system , 1989, IEA/AIE '89.

[22]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[23]  Leszek Rutkowski,et al.  Flexible neuro-fuzzy systems , 2003, IEEE Trans. Neural Networks.

[24]  J. Fodor.  On fuzzy implication operators , 1991 .

[25]  Robert Nowicki.  Rough Sets in the Neuro-Fuzzy Architectures Based on Monotonic Fuzzy Implications , 2004, ICAISC.

[26]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[27]  Didier Dubois,et al.  Putting Rough Sets and Fuzzy Sets Together , 1992, Intelligent Decision Support.

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

[29]  Masahiro Inuiguchi,et al.  Fuzzy rough sets and multiple-premise gradual decision rules , 2006, Int. J. Approx. Reason..