Global stability analysis of multitime-scale neural networks

• DOI: 10.1007/s00521-011-0680-9
• Publication Date: 2011-07-27T23:33:50Z
• Authors: Zhanshan Wang; Enlin Zhang; Huaguang Zhang; Zhengyun Ren
• Keywords: 0209 industrial biotechnology; 02 engineering and technology

Global asymptotic stability problem is studied for a class of recurrent neural networks with multitime scale. The concerned network involves two coupling terms, i.e., long-term memory and short-term memory, which leads to the difficulty to the dynamics analysis, especially for the case of multiple time varying delays. Some novel stability criteria are proposed on the basis of linear matrix inequality technique for the concerned neural network, which sufficiently consider the inhibitory actions in the different memories. From the viewpoint of biological information, the proposed results obviously improve the existing stability criteria. A numerical example is used to show the effectiveness of the obtained results.