Abstract
This paper focuses on the state estimation problem for complex-valued memristive neural networks with time-varying delays. By utilizing Lyapunov stability theory and some matrix inequality techniques, based on a novel Lyapunov functional, a sufficient delay-dependent condition which guarantees that the error-state system is global asymptotically stable is firstly derived for the addressed system, and a suitable state estimator is also designed. Finally, an example is given to illustrate the present method.
Highlights
During the past decades, a neural networks model has been studied intensively
A weighting delay and space partitioning method was proposed in [1, 2], and the stability criterion was established by establishing the relation among the connection parameters, delay parameters and dynamic variables of systems, which are less conservative than previous results
Considering the inevitability of time delay in many practical projects [50,51,52,53,54,55,56,57] and motivated by the above discussions, the state estimation problem for complex-valued memristive neural networks with time-varying delays is investigated in this paper
Summary
A neural networks model has been studied intensively. Broad applications have been explored in various areas ranging from signal processing, parallel computation and engineering optimization to pattern recognition, which rely heavily on the dynamical behaviors of this kind of model. The H∞ state estimation problem of discrete-time memristive neural networks is studied in [29]. For memristor-based complex-valued neural networks, abundant relevant results have been achieved [40,41,42,43,44,45,46].
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