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

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 in...

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Bibliographic Details
Published in:Journal of control, automation & electrical systems automation & electrical systems, 2023-04, Vol.34 (2), p.384-393
Main Authors: Brikh, Lamine, Guenounou, Ouahib, Bakir, Toufik
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Complex systems
Control
Control and Systems Theory
Dynamical systems
Electrical Engineering
Engineering
Mathematical models
Mechatronics
Nonlinear dynamics
Nonlinear systems
Parameters
Particle swarm optimization
Robotics
Robotics and Automation
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Summary: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.
ISSN:2195-3880
2195-3899
DOI:10.1007/s40313-022-00975-2