Robust Exponential Stability for Discrete-Time Quaternion-Valued Neural Networks with Time Delays and Parameter Uncertainties

摘要

In this paper, the robust exponential stability for discrete-time quaternion-valued neural networks with time delays and parameter uncertainties is investigated. By means of Lyapunov theorem, linear matrix inequality and contraction mapping theorem, new sufficient conditions are derived to ensure the existence, uniqueness and robust exponential stability of the equilibrium point of the proposed quaternion-valued neural networks. Compared with the existed literatures, the obtained results are less conservative. Finally, simulations are presented to illustrate the effectiveness of the theoretical results.