Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation

This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A M...

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Bibliographic Details
Published in:International journal of systems science 2010-04, Vol.41 (4), p.381-395
Main Authors: Lin, Cheng-Jian, Lee, Chi-Yung
Format: Article
Language:English
Subjects:
Control systems
Dynamical systems
Evolutionary
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Fuzzy
Neural networks
neuro-fuzzy control
non-linear systems
Nonlinear dynamics
Optimization
particle swarm optimisation
Physics
Solid dynamics (ballistics, collision, multibody system, stabilization...)
Solid mechanics
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Summary:This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.
ISSN:0020-7721
1464-5319
DOI:10.1080/00207720903045783