Delay-derivative-dependent stability for delayed neural networks with unbound distributed delay

Authors:
Tao Li;Aiguo Song;Shumin Fei;Ting Wang
Affiliations:
School of Instrument Science and Engineering, Southeast University, Nanjing, China;School of Instrument Science and Engineering, Southeast University, Nanjing, China;Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing, China;Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing, China
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

In this brief, based on Lyapunov-Krasovskii functional approach and appropriate integral inequality, a new sufficient condition is derived to guarantee the global stability for delayed neural networks with unbounded distributed delay, in which the improved delay-partitioning technique and general convex combination are employed. The LMI-based criterion heavily depends on both the upper and lower bounds on time delay and its derivative, which is different from the existent ones and has wider application fields than some present results. Finally, three numerical examples can illustrate the efficiency of the new method based on the reduced conservatism which can be achieved by thinning the delay interval.