Fixed‐time synchronization in multilayer networks with delay Cohen–Grossberg neural subnets via adaptive quantitative control
Fei Tan, Lili Zhou, Junwei Lu, Huiying Zhang
Abstract
Abstract 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.