Abstract
This paper studies the modeling and exponential stability problems for markovian jumping memristor-based neural networks (MJMNNs) via event-triggered impulsive control scheme (ETICS). The purpose is to design memristor-based neural networks (MNNs) which has markovian jumping parameters and hybrid time-vary delays to make the MNNs more general. Meanwhile, a state estimator is introduced to estimate system states through a vailable output measurements. Furthermore, the proposed event-triggered scheme (ETS), which is also determined by markovian parameters, is used to determine whether there is an impulse and whether the system need to transmit the sampled state information. Then, by using Lyapunov-Krasovskii functional (LKF) and an improved inequality, exponential stable criterions are established. Finally, a numerical example is given to support the results.
Highlights
The memristor was postulated as the fourth basic circuit element in electrical circuits by Chua in 1971 [1]
Motivated by the above theoretical analyses, we study the modeling and exponential stability problems for markovian jumping memristor-based neural networks (MJMNNs) via event-triggered impulsive control scheme (ETICS)
The major contributions of this paper are as follows: (1) We study the exponential stability of general memristor-based neural networks (MNNs) with markovian jumping and hybrid time-vary delays
Summary
The memristor was postulated as the fourth basic circuit element in electrical circuits by Chua in 1971 [1]. Paper [18] investigates the passivity of markovian jump systems with channel fading which uses event-triggered state feedback control. In order to solve the information latching problem, the markovian jumping will be considered in this paper. There are several results on global exponential stability of a fractional order cellular NNs with impulses and with time-varying and distributed delay has been presented in [24]. The paper [32] investigates the mixed H-infinity and passive filtering problem for a class of discrete-time networked singular markovian jump systems. The paper [33] investigates the event-triggered H ∞ control problem for networked discrete-time markov jump systems subject to repeated scalar nonlinearities. The major contributions of this paper are as follows: (1) We study the exponential stability of general MNNs with markovian jumping and hybrid time-vary delays. I and 0 are the identity and zero matrices with appropriate dimensions, respectively. co{E} is the closure of the convex hull of set E
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