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Title: Global convergence of Lotka-Volterra recurrent neural networks with delays
Authors: Yi, Z.
Tan, K.K. 
Keywords: Delay
Global convergence
Lotka-Volterra recurrent neural networks
Issue Date: Nov-2005
Citation: Yi, Z., Tan, K.K. (2005-11). Global convergence of Lotka-Volterra recurrent neural networks with delays. IEEE Transactions on Circuits and Systems I: Regular Papers 52 (11) : 2482-2489. ScholarBank@NUS Repository.
Abstract: Recurrent neural networks of the Lotka-Volterra model have been proven to possess characteristics which are desirable in some neural computations. A clear understanding of the dynamical properties of a recurrent neural network is necessary for efficient applications of the network. This paper studies the global convergence of general Lotka-Volterra recurrent neural networks with variable delays. The contributions of this paper are: 1) sufficient conditions are established for lower positive boundedness of the networks; 2) global exponential stability conditions are obtained for the networks. These conditions are totally independent of the variable delays which are therefore allowed to be uncertain; 3) novel Lyapunov functionals are constructed to establish delays dependent conditions for global asymptotic stability, and 4) simulation results and examples are provided to supplement and illustrate the theoretical contributions presented. © 2005 IEEE.
Source Title: IEEE Transactions on Circuits and Systems I: Regular Papers
ISSN: 10577122
DOI: 10.1109/TCSI.2005.853940
Appears in Collections:Staff Publications

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