Robust finite-time state estimation of uncertain neural networks with Markovian jump parameters

In this paper, the robust finite-time state estimation problem of the uncertain Markovian jump neural networks with partly unknown transition probabilities is investigated. In the neural networks, there are a set of modes, which are determined by Markov chain. First, we design a state observer to estimate the neuron states. Second, based on Lyapunov stability theory, a robust stability sufficient condition is derived such that the uncertain Markovian jump neural networks with partly unknown transition probabilities are robust finite-time stable. Then, the robust stability condition is expressed in terms of linear matrix inequalities (LMIs), which can be easily solved by standard software. Finally, a numerical example is given to demonstrate the effectiveness of the proposed new design techniques.