一类时变时滞模糊细胞神经网络的时滞依赖指数稳定性判据

Delay-Dependent Exponential Stability of Fuzzy Cellular Neural Networks with Time-Varying Delay

针对一类带有时变时滞的模糊细胞神经网络,通过适当的构造Lyapunov-Krasovskii泛函,以线性矩阵不等式的形式提出了一种新颖的依赖于时滞的全局指数稳定性判据.与之前结果相比,所提出的判据针对模糊时滞项进行了变换,从而首次考虑了模糊细胞神经网络中非模糊项的连接权矩阵中元素的符号问题,降低了判据的保守性.并且时滞变化率的限制将被放松.仿真结果进一步证明了判据的有效性.

The novel delay-dependent global exponential stability criteria are proposed for fuzzy cellular neural networks with time-varying delay. By constructing a new Lyapunov-Krasovskii functional, the criteria expressed by the form of linear matrix inequality （LMI） are given. Compared with the previous literature, the signs of dements of weighting matrix are first considered. Thus, the obtain results are less conservative. Moreover, the restriction of the time derivative of time-varying delays is released in the proposed criteria. The simulations are given to show the effectiveness of the criteria.