Fixed‐time synchronization of neural networks with time delay via quantized intermittent control

In this paper, fixed‐time synchronization of nonlinear stochastic coupling multilayer neural networks is studied. The neural subnets in the multilayer networks are delay Cohen–Grossberg neural networks (DCGNNs). To overcome uncertain factors, we designed an adaptive delay‐dependent controller in synchronization. To describe constraints of communication and other related problems in networks, which are due to limitations for bit rates and bandwidths in communication channels, an adaptive fixed‐time control strategy is purposed by introducing quantization signal input. A theoretical framework about fixed‐time synchronization in multilayer delay Cohen–Grossberg neural networks (MDCGNNs) is established. We find that fixed settling time is related to the scale of MDCGNNs, characteristics of the designed controller parameters, and level of quantization. Finally, the effective of the theoretical framework is validated in an example.

“…A theoretical framework about fixed-time synchronization in MDCGNNs is established. In the proof, the more appropriate inequalities to scale, the better results can be got [18][19][20].…”

Section: Discussionmentioning

confidence: 99%

Fixed‐time synchronization in multilayer networks with delay Cohen–Grossberg neural subnets via adaptive quantitative control

Tan,

Zhou,

et al. 2023

“…Synchronization means that two or more quantities that change over time maintain a certain relative relationship in the process of change. Synchronization of nonlinear dynamical systems (NDSs) has always been a hot topic because of its successful applications in many different areas such as biological systems, safety communication, laser physics, and modeling brain activity [1][2][3]. For synchronization control, there are many different control methods, such as continuous feedback control, impulsive control, sampled-data control, intermittent control, and adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Exponential synchronization of nonlinear dynamical systems via an aperiodic hybrid control with actuator saturation and impulse time windows

Wu,

Li,

et al. 2024

Considering impulse time windows and the actuator saturation, we put forward a novel aperiodic hybrid control scheme to achieve the exponential synchronization of two nonlinear dynamical systems (NDSs). Different from the existing research, the actuator saturation of two types of control signals and impulse time windows are both considered in the exponential synchronization analysis. Some sufficient conditions are found with designing the hybrid controller, and the attraction domain (AD) of the error system is also estimated. On the other hand, the conditions which can ensure the exponential synchronization between two NDSs with this hybrid control without saturation limit are also given. Finally, experimental results for the synchronization of three dimensional Hopfield‐type neural networks and the image reproduction based on the synchronization of neural networks demonstrate the feasibility of our theoretical results.

“…In [9], by making use of BAM NNs, an efficient reversible adaptive video watermarking scheme for multiple watermarks is analyzed. In many practical control systems, time delay exists unavoidably if there is the transmission of information between different parts of the system [10–12]. In view of this, the global robust stability analysis of BAM NNs with multiple time delays has been analyzed by Thoiyab et al [13].…”

Section: Introductionmentioning

confidence: 99%

Extended dissipative output‐feedback control for discrete‐time bidirectional associative memory neural networks via delay‐partitioning approach

Adhira

Nagamani

2023

This paper is concerned with the analysis of an extended dissipativity performance for a class of bidirectional associative memory (BAM) neural networks (NNs) having time‐varying delays. To achieve this, the idea of the delay‐partitioning approach is used, where the range of time‐varying delay factors is partitioned into a finite number of equidistant subintervals. A delay‐partitioning based Lyapunov–Krasovskii function is introduced on these intervals, and some new delay‐dependent extended dissipativity results are established in terms of linear matrix inequalities, which also depend on the partition size of the delay factor. Further, numerical examples are performed to acknowledge the extended dissipativity performance of delayed discrete‐time BAM NN; further, four case studies were explored with their simulations to validate the impact of the delay‐partitioning approach.