Non-fragile state estimation for delayed fractional-order memristive neural networks
R Li, X Gao, J Cao - Applied Mathematics and Computation, 2019 - Elsevier
R Li, X Gao, J Cao
Applied Mathematics and Computation, 2019•ElsevierThe issue of non-fragile estimation for fractional-order memristive system is provided in this
paper. By endowing the Lyapunov technique, the corresponding works that ensuring the
globally asymptotic stability of the error model are presented, which can be calculated
efficiently. In the end, the analytical methods are voiced by two simulations.
paper. By endowing the Lyapunov technique, the corresponding works that ensuring the
globally asymptotic stability of the error model are presented, which can be calculated
efficiently. In the end, the analytical methods are voiced by two simulations.
Abstract
The issue of non-fragile estimation for fractional-order memristive system is provided in this paper. By endowing the Lyapunov technique, the corresponding works that ensuring the globally asymptotic stability of the error model are presented, which can be calculated efficiently. In the end, the analytical methods are voiced by two simulations.
Elsevier
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