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1 2020

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Stabilization analysis of impulsive state-dependent neural networks with nonlinear disturbance: A quantization approach

Hong Yaxian, Bin Honghua, Huang Zhenkun

International Journal of Applied Mathematics and Computer Science

2020

Vol. 30, no. 2

267--279

In this paper, the problem of feedback stabilization for a class of impulsive state-dependent neural networks (ISDNNs) with nonlinear disturbance inputs via quantized input signals is discussed. By constructing quasi-invariant sets and attracting sets for ISDNNs, we design a quantized controller with adjustable parameters. In combination with a suitable ISS-Lyapunov functional and a hybrid quantized control strategy, we propose novel criteria on input-to-state stability and global asymptotical stability for ISDNNs. Our results complement the existing ones. Numerical simulations are reported to substantiate the theoretical results and effectiveness of the proposed strategy.