Stability Analysis for Stochastic Cohen-Grossberg Neural Networks With Mixed Time Delays  

Oleh:

Wang, Zidong ; Liu, Yurong ; Li, Maozhen ; Liu, Xiaohui

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 17 no. 3 (May 2006), page 814-820.
Topik: time; stability analysis; stochastic; cohen - grossberg; neural networks; mixed time delays

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this letter, the global asymptotic stability analysis problem is considered for a class of stochastic Cohen - Grossberg neural networks with mixed time delays, which consist of both the discrete and distributed time delays. Based on an Lyapunov - Krasovskii functional and the stochastic stability analysis theory, a linear matrix inequality (LMI) approach is developed to derive several sufficient conditions guaranteeing the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the addressed stochastic Cohen - Grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two LMI s are feasible, where the feasibility of LMI s can be readily checked by the Matlab LMI toolbox. It is also pointed out that the main results comprise some existing results as special cases. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria.

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