Abstract
The paper deals with a discrete-time recurrent neural network designed with dynamic neural models. Dynamics is reproduced within each single neuron, hence the considered network is a locally recurrent globally feed-forward. In the paper, conditions for global stability of the considered neural network are derived using the pole placement and Lyapunov second method.
This work was supported by the State Committee for Scientific Research in Poland (KBN) under the grant No. 4T11A01425
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Patan, K., Korbicz, J., Prętki, P. (2005). Global Stability Conditions of Locally Recurrent Neural Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_31
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DOI: https://doi.org/10.1007/11550907_31
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