Abstract
In this paper, the problems of stability of a general class of discrete-time delayed recurrent neural networks are re-investigated in light of some recent results. These networks are obtained by modeling synapses as Finite Impulse Response (FIR) filters instead of multiplicative scalars. We first derive a sufficient conditions for the network operating in closed-loop to converge to a fixed point using Lyapunov functional method; the symmetry of the connection matrix is not assumed. We then show how these conditions relate to other conditions ensuring both the existence of the error gradient other arbitrary long trajectories and the asymptotic stability of the fixed points at each time step.
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© 2003 Springer-Verlag Berlin Heidelberg
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Aussem, A. (2003). Closed Loop Stability of FIR-Recurrent Neural Networks. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_62
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DOI: https://doi.org/10.1007/3-540-44989-2_62
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