Analysis for Robust Stability of Hopfield Neural Networks with Multiple Delays  

作　　者：ZHANG Hua-Guang JI Ce ZHANG Tie-Yan 

机构地区：[1]Key Laboratory of Process industry Automation, Ministry of Education, Northeaste.rn University, Shenyang 110004

出　　处：《自动化学报》2006年第1期84-90,共7页Acta Automatica Sinica

基　　金：Supported by the National Natural Science Foundation of P.R.China (60274017, 60572070, 60325311) the Natural Science Foundation of Liaoning Province (20022030)

摘　　要：The robust stability of a class of Hopfield neural networks with multiple delays and parameter perturbations is analyzed. The sufficient conditions for the global robust stability of equilibrium point are given by way of constructing a suitable Lyapunov functional. The conditions take the form of linear matrix inequality (LMI), so they are computable and verifiable efficiently. Furthermore, all the results are obtained without assuming the differentiability and monotonicity of activation functions. From the viewpoint of system analysis, our results provide sufficient conditions for the global robust stability in a manner that they specify the size of perturbation that Hopfield neural networks can endure when the structure of the network is given. On the other hand, from the viewpoint of system synthesis, our results can answer how to choose the parameters of neural networks to endure a given perturbation.The robust stability of a class of Hopfield neural networks with multiple delays and parameter perturbations is analyzed. The sufficient conditions for the global robust stability of equilibrium point are given by way of constructing a suitable Lyapunov functional. The conditions take the form of linear matrix inequality （LMI）, so they are computable and vcrifiable efficiently. Furthermore, all the results are obtained without assuming the differentiability and monotonicity of activation functions. From the viewpoint of system analysis, our results provide sufficient conditions for the global robust stability in a manner that they specify the size of perturbation that Hopficld neural networks can endure when the structure of the network is given. On the other hand, from thc viewpoint of system synthesis, our results can answer how to choose the parameters of neural networks to endure a given perturbation.

关 键 词：神经网络 多重延迟 参数干扰 鲁棒控制 稳定性 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]