One-Step Weights Updating Back-Propagation Neural Network for Nonlinear System Identification

Yang Yang

doi:10.4028/www.scientific.net/AMM.734.558

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 734One-Step Weights Updating Back-Propagation Neural...

One-Step Weights Updating Back-Propagation Neural Network for Nonlinear System Identification

Abstract:

Given that many filters such as least mean squares and recursive least squares which are not able to deal with nonlinear system. In this paper, a nonlinear system identification technique using a specially designed neural network is investigated. Precisely, a power-activated back-propagation neural network is first constructed. Then, a high efficient weights updating method which only requires one-step iteration in its training session is presented. The system identification performance is evaluated through MATLAB simulations. The simulation results validate the one-step weights updating method and show satisfactory nonlinear system identification performance.

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Periodical:

Applied Mechanics and Materials (Volume 734)

Pages:

558-561

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.734.558

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Online since:

February 2015

Authors:

Yang Yang*

Keywords:

Back-Propagation Neural Network, Nonlinear System Identification, One-Step Weights Updating

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* - Corresponding Author

References

[1] L. Fu, and P.F. Li, The Research Survey of System Identification Method, International Conference on Intelligent Human-Machine Systems and Cybernetics, pp.97-401, Aug. (2013).

Google Scholar

[2] S.O. Haykin, Adaptive Filter Theory (4th Edition), Prentice Hall, Sept. (2001).

Google Scholar

[3] D.E. Rumelhart, J.L. McClelland, and PDP Research Group, Parallel Distributed Processing, MIT Press, (1986).

Google Scholar

[4] O. Nelles, Nonlinear System Identification, Springer, (1986).

Google Scholar