Title :
Stability Analysis of Markovian Jumping Stochastic Cohen–Grossberg Neural Networks With Mixed Time Delays
Author :
Zhang, Huaguang ; Wang, Yingchun
Author_Institution :
Northeastern Univ., Shenyang
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.
Keywords :
Markov processes; Newton method; asymptotic stability; delays; linear matrix inequalities; neural nets; Markovian jumping stochastic Cohen-Grossberg neural network; Matlab LMI toolbox; Newton-Leibniz formula; asymptotic stability analysis problem; discrete delay; distributed delay; free-weight matrix; linear matrix inequality; mixed time delay; system transformation; Cohen–Grossberg neural networks (CGNNs); Markovian jumping; delay-dependent criteria; linear matrix inequality (LMI); mixed delay; Computer Simulation; Markov Chains; Neural Networks (Computer); Nonlinear Dynamics; Time Factors;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.910738
