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Proceedings Paper

Application of dynamic recurrent neural networks in nonlinear system identification
Author(s): Yun Du; Xueli Wu; Huiqin Sun; Suying Zhang; Qiang Tian
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Paper Abstract

An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteristics of system can reflect the dynamic characteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNN, and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topological structure in usual dynamic recurrent neural network.

Paper Details

Date Published: 6 November 2006
PDF: 8 pages
Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 635754 (6 November 2006); doi: 10.1117/12.717521
Show Author Affiliations
Yun Du, Hebei Univ. of Science and Technology (China)
Xueli Wu, Hebei Univ. of Science and Technology (China)
Huiqin Sun, Hebei Univ. of Science and Technology (China)
Suying Zhang, Hebei Univ. of Science and Technology (China)
Qiang Tian, Shijiazhuang Realty Administerial Office (China)


Published in SPIE Proceedings Vol. 6357:
Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence

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