Uniform Quantized Synchronization for Chaotic Neural Networks with Successive Packet Dropouts

Abstract

In this paper, the problem of uniform quantized synchronization is investigated for chaotic neural networks with packet dropouts. By means of the stochastic analysis approach and inequality technique, sufficient conditions are derived under which the synchronization error system is exponentially ultimately bounded in mean square. Finally, a numerical example is provided to validate the feasibility and effectiveness of the proposed results.

Decentralized dynamic event‐triggered control for networked control systems under unreliable communication network and actuator saturation

“…Consequently, this paper studies the influence of multi‐channel fading and hybrid cyber attacks on the NCSs, which is more general. In addition, when the multi‐channel fading degenerates into random packet dropout phenomenon [44], it is similar to DoS attack. So this paper considers the mixture of deception attack and replay attack.…”

Distributed accelerating of quantized second‐order consensus with bounded input

“…where w ∈ R . It should be noted that although (18) is continuous everywhere, it is not differentiable at the discontinuous points of the quantizer. As a result, by using the generalized gradient defined in [25], we will obtain:…”

“…In [14] the authors tried to find the bound of quantization error for a system with first‐order discrete‐time agents. Moreover, in [18], the authors attempted to solve the synchronization problem of chaotic neural networks using quantized information. They showed that the synchronization error could be exponentially ultimately bounded in mean square criteria.…”

“…As a result, employing real-valued data is not applicable in practical experiments. To overcome this issue, the quantized consensus problem is proposed in which each agent receives the discretized data of its neighbors and uses only a limited number of data in its control input, [14][15][16][17][18][19]. In [14] the authors tried to find the bound of quantization error for a system with first-order discrete-time agents.…”

Synchronization of non‐linear systems with disturbances subjected to dead‐zone and saturation characteristics in control input using DMVT approach

“…Recently, an adaptive synchronization for general complex networks with uncertainties was introduced in [9]. Further, synchronization of chaotic neural networks by means of stochastic analysis and inequality technique was presented in [10]. An adaptive fuzzy output feedback synchronization of non‐linear multi‐agent systems with input saturation was dealt in [11].…”