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Paper details
Number 2 - June 2020
Volume 30 - 2020
Stabilization analysis of impulsive state-dependent neural networks with nonlinear disturbance: A quantization approach
Yaxian Hong, Honghua Bin, Zhenkun Huang
Abstract
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.
Keywords
state-dependent neural networks, quantized input, stabilization