Abstract
The problem of robust exponential stability analysis for uncertain stochastic neural networks is investigated based on Lyapunov stability theory. The parametric uncertainties in the neural networks satisfy the Frobenius norm-bounded conditions. The exogenous disturbance and stochastic perturbation functions satisfy the Liptistz conditions. Based on linear matrix inequality approach, the sufficient exponential stable criteria and the asymptotical stability condition on uncertain stochastic neural networks are presented.
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© 2011 Springer-Verlag Berlin Heidelberg
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Tang, X., Xie, L. (2011). Robust Exponential Stability Analysis for Uncertain Stochastic Neural Networks. In: Zhu, M. (eds) Information and Management Engineering. ICCIC 2011. Communications in Computer and Information Science, vol 235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24022-5_1
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DOI: https://doi.org/10.1007/978-3-642-24022-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24021-8
Online ISBN: 978-3-642-24022-5
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