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Approximation to Nonlinear Discrete-Time Systems by Recurrent Neural Networks

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The Sixth International Symposium on Neural Networks (ISNN 2009)

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 56))

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Abstract

Neural networks are widely used to approximate nonlinear functions. In order to study its approximation capability, a approximating approach for nonlinear discrete-time systems is presented by using the concept of the time-variant recurrent neural networks (RNNs) and the theory of two-dimensional systems. Both theory and simulations results show that the derived mathematical model of RNNs can approximate the nonlinear dynamical systems to any degree of accuracy.

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Li, F. (2009). Approximation to Nonlinear Discrete-Time Systems by Recurrent Neural Networks. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_55

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  • DOI: https://doi.org/10.1007/978-3-642-01216-7_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01215-0

  • Online ISBN: 978-3-642-01216-7

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