State estimation for complex-valued memristive neural networks with time-varying delays

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.

Variance-constrained resilient $H_{\infty }$ state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

“…For example, compared with the non-fragile/resilient state estimation method in [10], our estimation scheme has the advantage to reveal the whole impacts from missing measurements and randomly varying nonlinearities onto the estimation algorithm performance, which can present a new treatment way. In contrast to the results in [11,12], the superiority dealing with the time-varying characteristics can be observed from our new state estimation scheme.…”

“…Moreover, some useful state estimation algorithms have been given in [8] for delayed NNs to guarantee the H ∞ as well as passivity and in [9] for bidirectional associative NNs subject to mixed time-delays. During the analysis and implementation of the methods related to RNNs, it should be noticed that the neuron states may not always available in reality, so there is a need to estimate them by utilizing effective estimation methods [10][11][12]. Until now, many results have been published with respect to the state estimation problem of different types of dynamical networks [13][14][15][16].…”

Parameter-range-dependent robust stability conditions for quaternion-valued neural networks with time delays

“…Since neural networks had been proposed in the 1940s, they have been widely studied by many researchers, due to the many applications in various areas, such as associative memory, optimization, pattern recognition, fault diagnosis and signal processing. A lot of excellent work as regards the real-valued neural networks (RVNNs) and complex-valued neural networks (CVNNs) has appeared in the study of their dynamics [1][2][3][4][5][6]. However, there are some problems that the RVNNs and the CVNNs cannot deal with straightforwardly, such as 4-D signals, body images which are four or more dimensional [7][8][9], new methods or theories have to be put forward to, the theory of quaternion-valued neural networks (QVNNs) is one of those approaches, since it can handle not only real-valued and complex-valued cases but also the multidimensional data.…”

Variance-constrained filtering for nonlinear systems with randomly occurring quantized measurements: recursive scheme and boundedness analysis

“…Over the past few years, the state estimation or filtering problems have been widely discussed owing to its practical applications in various fields, such as in navigation system, dynamic positioning, tracking of objects in computer vision, and so on [1][2][3][4][5][6][7]. In particular, based on a series of observed measurements over time, the Kalman filtering known as a linear optimal estimation algorithm can provide the globally optimal estimation for linear stochastic systems [8].…”