A modified particle swarm optimization-based dynamic recurrent neural network for identifying and controlling nonlinear systems

In this paper, we first present a learning algorithm for dynamic recurrent Elman neural networks based on a modified particle swarm optimization. The proposed algorithm computes concurrently both the evolution of network structure, weights, initial inputs of the context units and self-feedback coeff...

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Vydáno v:Computers & structures Ročník 85; číslo 21; s. 1611 - 1622
Hlavní autoři: Ge, Hong-Wei, Liang, Yan-Chun, Marchese, Maurizio
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.11.2007
Elsevier Science
Témata:
Acoustics
Computational techniques
Dynamic recurrent neural network
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Mathematical methods in physics
Measurement and testing methods
Nonlinear system identification
Particle swarm optimization
Physics
Solid mechanics
Structural and continuum mechanics
System control
Transduction; acoustical devices for the generation and reproduction of sound
Ultrasonic motor
Dynamic recurrent neural network
System control
Ultrasonic motor
Particle swarm optimization
Nonlinear system identification
Recurrent neural nets
Acoustic measurement
Piezoelectric sensor
Control synthesis
Experimental study
Modeling
Micromotor
Feedback
Ultrasonic motors
Ultrasonic control
Swarm intelligence
Speed control
System identification
Non linear effect
Non destructive test
ISSN:0045-7949, 1879-2243
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Popis
Shrnutí:In this paper, we first present a learning algorithm for dynamic recurrent Elman neural networks based on a modified particle swarm optimization. The proposed algorithm computes concurrently both the evolution of network structure, weights, initial inputs of the context units and self-feedback coefficient of the modified Elman network. Thereafter, we introduce and discuss a novel control method based on the proposed algorithm. More specifically, a dynamic identifier is constructed to perform speed identification and a controller is designed to perform speed control for Ultrasonic Motors (USM). Numerical experiments show that the novel identifier and controller based on the proposed algorithm can both achieve higher convergence precision and speed than other state-of-the-art algorithms. In particular, our experiments show that the identifier can approximate the USM’s nonlinear input–output mapping accurately. The effectiveness of the controller is verified using different kinds of speeds of constant, step and sinusoidal types. Besides, a preliminary examination on a randomly perturbation also shows the robust characteristics of the two proposed models.
Bibliografie:ObjectType-Article-2
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content type line 23
ISSN:0045-7949
1879-2243
DOI:10.1016/j.compstruc.2007.03.001