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研究了一类具有不连续激励函数的时滞Cohen-Grossbe神经网络,利用推广的Lyapunov方法,证明了Filippov意义下解全局收敛到惟一的平衡点.在证明过程中使用了链式法.利用该法则可以计算不可微Lyapunov函数对时间t沿右端不连续微分方程的导数.
A class of delayed Cohen-Grossbe neural networks with discontinuous excitation functions is studied. By using the generalized Lyapunov method, it is proved that the solution converges to the only equilibrium point in the Filippov sense. The chain method is used in the proof process. The law can compute the derivative of non-differentiable Lyapunov functions on the right-end discontinuous differential equation at time t.