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New Aspects of Interpretability of Fuzzy Systems for Nonlinear Modeling

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Advances in Data Analysis with Computational Intelligence Methods

Part of the book series: Studies in Computational Intelligence ((SCI,volume 738))

Abstract

Fuzzy systems are well suited for nonlinear modeling. They can be effectively used if their structure and structure parameters are properly chosen. Moreover, it should be ensured that system rules are clear and interpretable. In this paper we propose a new algorithm for automatic learning and new interpretability criteria of fuzzy systems. Interpretability criteria are related to all aspects of those systems, not only their fuzzy sets and rules. Therefore, proposed criteria also concern parameterized triangular norms, discretization points and weights of importance from the rule base. As of the present time similar solutions have not been discussed in the literature. The proposed criteria are taken into account in the learning process, which is carried out with the use of a new learning algorithm. It was created by combining the genetic and the firework algorithms (this particular combination makes it possible to automatically choose not only system parameters but also its structure). It is an important advantage as most of the learning algorithms can only select system parameters when their structure has been specified by the designer. Proposed solutions were tested using typical simulation problems of nonlinear modeling.

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Acknowledgements

The project was financed by the National Science Centre (Poland) on the basis of the decision number DEC-2012/05/B/ST7/02138.

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Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Krystian Łapa, Krzysztof Cpałka & Leszek Rutkowski

  2. Information Technology Institute, Academy of Social Sciences, Ul. Sienkiewicza 9, 90-113, Łódź, Poland

    Leszek Rutkowski

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  1. Krystian Łapa
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  2. Krzysztof Cpałka
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  3. Leszek Rutkowski
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Correspondence to Leszek Rutkowski .

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Editors and Affiliations

  1. Department of Medicine, Division of Nephrology and Hypertension, University of Louisville, Louisville, Kentucky, USA

    Adam E Gawęda

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Institute of Computational Intelligence, Department of Mechanical Engineering and Computer Science, Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  4. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma, USA

    Gary G. Yen

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Łapa, K., Cpałka, K., Rutkowski, L. (2018). New Aspects of Interpretability of Fuzzy Systems for Nonlinear Modeling. In: Gawęda, A., Kacprzyk, J., Rutkowski, L., Yen, G. (eds) Advances in Data Analysis with Computational Intelligence Methods. Studies in Computational Intelligence, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-67946-4_9

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