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A Type-2 Fuzzy Systems Approach for Clustering-Based Identification of a T-S Regression Model

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Computational Intelligence: Theories, Applications and Future Directions - Volume I

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 798))

Abstract

The use of clustering for structure identification in T-S fuzzy models has been demonstrated to be very effective. More so, in recent times fuzzy c-regression models that use Type-2 fuzzy clustering have been demonstrated to yield remarkable results. In this chapter, a modified framework for the fuzzy c-regression model is developed and an innovative Gaussian-shaped hyperplane membership function is proposed. Interval Type-2 fuzzy c-means is used for estimating the coefficients of the upper and lower hyperplanes. The novelty of our method lies in the fact that defuzzification of model output has been delayed until the very end, before which repeated iterations of Karnik–Mendel Algorithm and Kalman Filter are used for optimizing the consequent parameters. The results obtained on benchmark problems are better than the state of the art, as has been demonstrated in the paper.

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Authors and Affiliations

  1. Indian Institute of Technology, Kanpur, India

    Homanga Bharadhwaj, Vikas Singh & Nishchal K. Verma

Authors
  1. Homanga Bharadhwaj
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  2. Vikas Singh
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  3. Nishchal K. Verma
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Corresponding author

Correspondence to Vikas Singh .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

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Bharadhwaj, H., Singh, V., Verma, N.K. (2019). A Type-2 Fuzzy Systems Approach for Clustering-Based Identification of a T-S Regression Model. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_28

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