title: Stability analysis of Markovian jumping stochastic Cohen-Grossberg neural networks with mixed time delays.
abstract: In this letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free-weight matrix via Newton-Leibniz formula is required. Two numerical examples are included to show the effectiveness of the result.
authors.affiliation: Automatic Control Department, Northeastern University, Shenyang, China. hgzhang@ieee.org
authors.forename: H
authors.initials: H
authors.lastname: Zhang
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
mesh_terms: D003198:Computer Simulation; D008390:Markov Chains; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors