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Selection of Minimum Rules from a Fuzzy TSK Model Using a PSO–FCM Combination

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Abstract

This work proposes an efficient and promising method for linear and nonlinear dynamic systems modeling. Unlike previous methods, we combine Fuzzy C-Means (FCM) and modified Particle Swarm Optimization (PSO) algorithms. The FCM allows clustering large nonlinear data sets (good rule-base parameters initialization). The PSO algorithm is used to optimize the parameters of the fuzzy system rules (initialized by FCM). This combination allows reducing the rule-base of TSK fuzzy model considering the three goals: have a good initialization of the parameters to be optimized, handling complex systems with ensuring a high accuracy and a low complex algorithm structure. Indeed, from a given fuzzy rule-base, FCM–PSO selects a subset of important fuzzy rules based on the threshold criterion of each rule. The interaction between the particles improves iteratively the quality of each fuzzy rule. Simulation results using two well-known benchmark functions show the efficiency of the proposed approach when compared to previous works.

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Authors and Affiliations

  1. Laboratoire de Technologie Industrielle et de l’Information (LTII), Faculté de Technologie, Université de Bejaia, 06000, Bejaïa, Algeria

    Lamine Brikh & Ouahib Guenounou

  2. Laboratoire ImViA, EA 7535, Université de Bourgogne, Dijon, France

    Toufik Bakir

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  1. Lamine Brikh
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  2. Ouahib Guenounou
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  3. Toufik Bakir
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Correspondence to Lamine Brikh.

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Brikh, L., Guenounou, O. & Bakir, T. Selection of Minimum Rules from a Fuzzy TSK Model Using a PSO–FCM Combination. J Control Autom Electr Syst 34, 384–393 (2023). https://doi.org/10.1007/s40313-022-00975-2

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