时变时滞神经网络的时滞相关鲁棒稳定性和耗散性分析

Delay-dependent robust stability and dissipativity analysis of neural networks with time-varying delays

研究时变时滞神经网络的鲁棒稳定性和耗散性问题.充分利用积分项的时滞信息和激励函数条件构造一个合适的增广LK泛函;利用自由矩阵积分不等式处理LK泛函的导数,得到一个低保守性的时滞相关稳定判据;将所获得的结论延伸至神经网络的耗散性分析,并推导出一个确保神经网络严格(X,Y,Z)-γ-耗散的充分条件.最后通过3个数值算例验证了所提出方法的可行性和优越性.

The problems of robust delay-dependent stability and dissipativity for neural networks（NNs） with time-varying delay is investigated. A proper augmented Lyapunov-Krasovskii functional（LKF） is constructed, which fully utilizes the information of time-delay in integral term and the neuron activation function conditions. Then, by employing the free- matrix-based integral inequality to handle the derivative of the LKF, a less conservative delay-dependent stability criteria is obtained. The acquired conclusion is extended to the analysis of dissipativity for delayed NNs, and a suficient condition is derived to guarantee the NNs strictly（X, y, Z）--y-dissipative. The superiority and feasibility of presented approaches are verified via three numerical examples.