Application of dynamic recurrent neural networks in nonlinear system identification

Yun Du; Xueli Wu; Huiqin Sun; Suying Zhang; Qiang Tian

doi:10.1117/12.717521

6 November 2006 Application of dynamic recurrent neural networks in nonlinear system identification

Yun Du, Xueli Wu, Huiqin Sun, Suying Zhang, Qiang Tian

Proceedings Volume 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence; 635754 (2006) https://doi.org/10.1117/12.717521
Event: Sixth International Symposium on Instrumentation and Control Technology, 2006, Beijing, China

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.

Citation Download Citation

Yun Du, Xueli Wu, Huiqin Sun, Suying Zhang, and Qiang Tian "Application of dynamic recurrent neural networks in nonlinear system identification", 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); https://doi.org/10.1117/12.717521

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