On the exponential stability of neural networks systems with time-varying delay

In this paper, exponential stability of Hopfield-type neural networks with time-varying delays are analyzed. By using the Lyapunov functional method, sufficient conditions are obtained for general exponential stabilities. At the same time, the output functions do not satisfy the Lipschitz conditions and do not require them to be differential or strictly monotonously increasing. Moreover, all results are established without assuming any symmetry of the connection matrix.A numeric example is pressented to show the effective of these criteria.     

具有输入时滞的随机非线性系统的自适应神经网络控制

考虑一类具有输入时滞的随机非线性系统的自适应神经网络控制问题。通过定义含输入积分项的设计变量,将输入时滞系统转变为非时滞系统。结合神经网络控制、积分中值定理与Decoupled Backstepping技巧,针对该类系统提出一套自适应控制策略。所提出的控制器保证闭环系统的所有信号皆4阶矩半全局一致最终有界,并且跟踪误差收敛于原点附近的小邻域内。仿真实验结果验证了所提出控制策略的有效性。