Abstract
Novel convergence properties of identification algorithm for complex input-output systems, which uses recurrent neural networks, are derived. By the term “complex system” we understand a system containing interconnected sub processes (elementary processes), which can operate separately. Each element of the complex system is modeled by a multi-input, multi-output neural network. A model of the whole system is obtained by composing all neural networks into one global network. Stable learning algorithm of such a neural network is proposed. We derived sufficient condition of stability using the second Lyapunov method and proved that algorithm is stable even if stability conditions for some individual neural networks are not satisfied.
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Drapała, J., Świa̧tek, J., Brzostowski, K. (2008). Stable Learning Algorithm of Global Neural Network for Identification of Dynamic Complex Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_3
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DOI: https://doi.org/10.1007/978-3-540-69731-2_3
Publisher Name: Springer, Berlin, Heidelberg
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