Analysis of Adaptive Synchronization for Stochastic Neutral-Type Memristive Neural Networks with Mixed Time-Varying Delays

Abstract

Linear feedback control and adaptive feedback control are proposed to achieve the synchronization of stochastic neutral-type memristive neural networks with mixed time-varying delays. By applying the stochastic differential inclusions theory, Lyapunov functional, and linear matrix inequalities method, we obtain some new adaptive synchronization criteria. A numerical example is given to illustrate the effectiveness of our results.

Analysis of Exponential Stability for Neutral Stochastic Cohen-Grossberg Neural Networks with Mixed Delays

“…The constant delay is constant and discrete, which means that the effect of the state is delayed by a fixed value on the system. It can be observed that most of the literature works mainly focus on three simple cases containing constant delays, distributed delays, and time-varying delays (see [20][21][22]). In fact, mixed delays have been considered to be more efficient in modeling neural network systems because these simple delays are often not feasible when neural network systems become more complex.…”

Dissipativity analysis of neutral-type memristive neural network with two additive time-varying and leakage delays

“…Dissipative system theory provides a framework for the design and analysis of control systems based on energy-related considerations [17]. At present, although there are some studies on the dissipativity of neural networks [18][19][20], most of them are focusing on the synchronization of neural networks [21][22][23][24]. For the dissipativity analysis of neural networks, it is essential to find global exponentially attracting sets.…”