Abstract
Many physical systems contains fast and slow phenomenons. In this paper we propose a dynamic neural networks with different time-scales to model the nonlinear system. Passivity-based approach is used to derive stability conditions for neural identifer. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. Numerical examples are also given to demonstrate the effectiveness of the theoretical results.
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Sandoval, A.C., Yu, W., Li, X. (2006). Passivity Analysis for Neuro Identifier with Different Time-Scales. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_51
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DOI: https://doi.org/10.1007/11816157_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
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