Parameter optimization of a fuzzy inference system using the FisPro open source software

This paper proposes a flexible optimization sequence that can be applied to any parameter of a fuzzy inference system. Interrelated parameters can be optimized together, and criteria include system accuracy and coverage. The fuzzy inference system structure is preserved and constraints are imposed to respect the fuzzy partition semantics. The procedure described here uses a Solis & Wets based algorithm, but the approach remains valid for other optimization techniques, provided that they accept semantic constraints. The optimization sequence is implemented in an open source software, FisPro, made for fuzzy inference system design and tuning.

[1]  Luis Magdalena,et al.  Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview , 2003 .

[2]  Didier Dubois,et al.  Practical Inference With Systems of Gradual Implicative Rules , 2009, IEEE Transactions on Fuzzy Systems.

[3]  Phillip Ein-Dor.  Grosch's law re-revisited: CPU power and the cost of computation , 1985, CACM.

[4]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[5]  Didier Dubois,et al.  A New Perspective on Reasoning with Fuzzy Rules , 2002, AFSS.

[6]  Sébastien Destercke,et al.  Building an interpretable fuzzy rule base from data using Orthogonal Least Squares - Application to a depollution problem , 2007, Fuzzy Sets Syst..

[7]  SergeGuillaume,et al.  Fuzzy Models to Deal with Sensory Data in Food Industry , 2004 .

[8]  Brigitte Charnomordic,et al.  Learning interpretable fuzzy inference systems with FisPro , 2011, Inf. Sci..

[9]  Olga Kosheleva,et al.  IEEE International Conference on Fuzzy Systems , 1996 .

[10]  Sheng Chen,et al.  Orthogonal least squares methods and their application to non-linear system identification , 1989 .

[11]  Chia-Feng Juang,et al.  Designing Fuzzy-Rule-Based Systems Using Continuous Ant-Colony Optimization , 2010, IEEE Transactions on Fuzzy Systems.

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

[13]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[14]  Richard Weber,et al.  Fuzzy-ID3: A class of methods for automatic knowledge acquisition , 1992 .

[15]  J. Mendel,et al.  Two-pass orthogonal least-squares algorithm to train and reduce fuzzy logic systems , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[16]  Luis Magdalena,et al.  Expert guided integration of induced knowledge into a fuzzy knowledge base , 2006, Soft Comput..

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

[18]  Witold Pedrycz,et al.  A Gradient-Descent-Based Approach for Transparent Linguistic Interface Generation in Fuzzy Models , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Joachim Weisbrod,et al.  A new approach to fuzzy reasoning , 1998, Soft Comput..

[20]  Francisco Herrera,et al.  A Multiobjective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems , 2009, IEEE Transactions on Fuzzy Systems.

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

[22]  Nelson F. F. Ebecken,et al.  Design of interpretable fuzzy rule-based classifiers using spectral analysis with structure and parameters optimization , 2009, Fuzzy Sets Syst..

[23]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.