tailieunhanh - Exponential stabilization of Neural Networks with mixed Time-Varying Delays in state and control

This paper presents some results on the global exponential stabilization for cellular networks with various activation functions and mixed timevarying delays in state and control. | Exponential stabilization of Neural Networks with mixed Time-Varying Delays in state and control MAI V. THUAN 1,∗ 1,∗ , N. T. T. HUYEN 1 and N. T. M. NGOC 2 College of Sciences, Thainguyen university, Thainguyen, Vietnam 2 Thainguyen university of Technology, Thainguyen, Vietnam ∗ Corresponding author: maithuank1@ Abstract. This paper presents some results on the few results are published. The papers [1, 6, 7] present global exponential stabilization for cellular networks with various activation functions and mixed timevarying delays in state and control. Based on augmented time-varying Lyapunov Krasovskii functionals, new delay-dependent conditions for the global exponential stabilization are obtained in terms of linear matrix inequalities. Numerical examples are given to illustrate the feasibleness of our results. some stabilization criteria for delayed neural networks. However, the results reported therein not only require the only activation function, but the system matrices are also strictly constrained. In this paper, we consider a stabilization scheme for a general class of delayed neural networks. The novel features here are that the neural networks in consideration are time-varying with mixed delay in state and control and with various activation function. We extend the results of [1, 2, 6, 7, 9] to exponential stabilization of neural networks various activation funcitons and mixed time-varying delay in state and control. Using the Lyapunov stability theory and linear matrix inequality (LMI) techniques, a control law with an appropriate gain control matrix is derived to achieve stabilization of the neural networks with mixed timevarying delayed in state and control. The stabilization criteria are obtained in terms of LMIs and hence the gain control matrix is easily determined by numerical Matlab's Control Toolbox. Cellular neutral networks, stabilization, neural networks, mixed delay, Lyapunov function, Linear matrix .