Abstract
In this paper, we first present a learning algorithm for dynamic recurrent Elman neural networks based on a dissimilation 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. 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 and sinusoidal types. Besides, a preliminary examination on a randomly perturbation also shows the robust characteristics of the two proposed models.
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© 2007 Springer-Verlag Berlin Heidelberg
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Hong-Wei, G., Wen-Li, D., Feng, Q., Lu, W. (2007). A Dissimilation Particle Swarm Optimization-Based Elman Network and Applications for Identifying and Controlling Ultrasonic Motors. In: Melin, P., Castillo, O., RamÃrez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_40
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DOI: https://doi.org/10.1007/978-3-540-72432-2_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72431-5
Online ISBN: 978-3-540-72432-2
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