具比例时滞的Cohen-Grossberg神经网络的稳定性研究  

Stability Analysis of Cohen-Grossberg Neural Networks with Proportional Delays

作　　者：古力加依娜•木合亚提 姑丽加玛丽•麦麦提艾力 

机构地区：[1]新疆师范大学数学科学学院,新疆 乌鲁木齐

出　　处：《理论数学》2023年第4期996-1006,共11页Pure Mathematics

摘　　要：本文研究了具有比例时滞的Cohen-Grossberg神经网络的全局指数稳定性。首先通过适当的变换,将比例时滞的Cohen-Grossberg神经网络模型等价的转换为具有常时滞的Cohen-Grossberg神经网络模型。通过应用M-矩阵理论和不等式技巧建立了全局指数稳定性的充分条件。通过数值仿真来验证了所得结论的有效性。This paper investigates global exponential stability of Cohen-Grossberg neural networks with proportional delays. Firstly, the Cohen-Grossberg neural networks model with proportional delay is equivalent to the Cohen-Grossberg neural networks model with constant delay through appropriate transformation. Sufficient conditions for global exponential stability are established by applying M-matrix theory and inequality techniques. The validity of the obtained conclusions is verified by numerical simulation.

关 键 词：COHEN-GROSSBERG神经网络 全局指数稳定 比例时滞 M-矩阵理论 

分 类 号：G63[文化科学—教育学]