Abstract
The state estimation problem for discrete neural networks with Markovian jumping parameters and time-varying delays is investigated. The considered transition probabilities of the mode jumps are assumed to be partially unknown. The purpose of the state estimation problem is to design a state estimator to estimate the neuron states ensuring the dynamics of the estimation error stochastically stable. In terms of a novel Lyapunov functional, the delay-dependent sufficient conditions for the existence of desired state estimator are derived. A numerical example is given to show the validness of the established results.
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