CAJ | 학술논문

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.
연구료일류구유불련속격려함수적시체Cohen-Grossbe신경망락,이용추엄적 Lyapunov방법,증명료Filippov의의하해전국수렴도유일적평형점.재증명과정중사용료련식법.이용해법칙가이계산불가미Lyapunov함수대시간t연우단불련속미분방정적도수.
A class of Cohen-Grossberg neural networks where the neuron activations are modeled by discontinuous functions was considered. A tool,the chain rule for computing the time derivative along the neural network solutions of a nondifferentiable Lyapunov function,is used which enables us to apply a Lyapunov-like approach to differential equations with discontinuous right-hand side. By means of the Lyapunov-like approach,a general result is proved on global exponential convergence toward a unique equilibrium point of the neural network solutions in the sense of Filippov.