Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 22, Issue 7, Pages 1032-1045Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2011.2131679
Keywords
Distributed delays; global asymptotic stability; high-order neural networks; Lebesgue-Stieljies measures; multiple delays; neutral-type delay; recurrent neural networks
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Funding
- National Natural Science Foundation of China [50977008, 61074073, 61034005, 90816023]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [200801451096]
- China Postdoctoral Science Foundation [200902547]
- Basic Scientific Research of Central Colleges of China [N090404017, N100104102]
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Global asymptotic stability problem is studied for a class of recurrent neural networks with distributed delays satisfying Lebesgue-Stieljies measures on the basis of linear matrix inequality. The concerned network model includes many neural network models with various delays and structures as its special cases, such as the delays covering the discrete delays and distributed delays, and the network structures containing the neutral-type networks and high-order networks. Therefore, many new stability criteria for the above neural network models have also been derived from the present stability analysis method. All the obtained stability results have similar matrix inequality structures and can be easily checked. Three numerical examples are used to show the effectiveness of the obtained results.
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