State estimation for neural neutral-type networks with mixed time-varying delays and Markovian jumping parameters
Lakshmanan Shanmugam, Ju H. Park, Ho-Youl Jung, Pagavathigounder Balasubramaniam · Chinese Physics B · 2012
This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed time-varying delays and Markovian jumping parameters. The addressed neural networks have a finite number of modes, and the modes may jump from one to another according to a Markov process. By construction of a suitable Lyapunov—Krasovskii functional, a delay-dependent condition is developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable in a mean square. The criterion is formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages.