多时滞Cohen-Grossberg神经网络的全局渐近稳定性分析(英文)

Global Stability Analysis of Cohen-Grossberg Neural Networks Involving Multiple Delays

研究多时滞Cohen-Grossberg神经网络的全局渐近稳定性,利用新的不等式技术,同伦映射及李雅普诺夫泛函方法,我们获得了几个新的准则.同已有的结果相比,我们不要求函数有界,可微或严格增加等条件.我们的结果更容易验证且具有较少的限制.此外,给出了一个例子来证明我们的结果的优势.

In this paper, we study the global asymptotic stability of Cohen-Grossbergneural networks involving multiple delays. By using new inequality techniques, homotopic mapping and constructing a new type Lyapunov functional method, we obtain some new criteria guaranteeing global asymptoticstability. In our results, we do not require activation functions to be bounded, differentiable or strictly increasing. The obtained results are better than those in literatures. The presented results are more easily to verify and turn out tobe tess restrictive than those given in the earlier literature. One example is also worked out to demonstrate the advantages of our resuits.