Adaptive control of nonlinear system using neuro-fuzzy learning by PSO algorithm

This paper proposes the optimization of parameters of neuro-fuzzy system using the particle swarm optimization. Neuro-fuzzy techniques have emerged from the fusion of neural networks and fuzzy inference systems. They could serve as a powerful tool for system modeling and control. These fuzzy systems...

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Bibliographic Details
Published in2012 16th IEEE Mediterranean Electrotechnical Conference pp. 519 - 523
Main Authors Turki, M., Bouzaida, S., Sakly, A., M'Sahli, F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2012
Subjects
Adaptation models
Control systems
Inference algorithms
Inverse problems
Mathematical model
Particle swarm optimization
Training
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ISBN9781467307826
1467307823
ISSN2158-8473
DOI10.1109/MELCON.2012.6196486

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Summary:This paper proposes the optimization of parameters of neuro-fuzzy system using the particle swarm optimization. Neuro-fuzzy techniques have emerged from the fusion of neural networks and fuzzy inference systems. They could serve as a powerful tool for system modeling and control. These fuzzy systems are optimized by adapting the antecedent and consequent parameters. Among them, the ANFIS use the least square to optimize the consequent parameters and retropropagation to train the antecedent parameters. Several learning algorithms of fuzzy models have been proposed, e.g. evolutionary algorithms, such as particle swarm optimization. These different methods have been developed to learn the parameters of neuro-fuzzy system and to test them in the on-line control of nonlinear system.
ISBN:9781467307826
1467307823
ISSN:2158-8473
DOI:10.1109/MELCON.2012.6196486