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Circuits, Communications and Systems, Pacific-Asia Conference on (2009)
Chengdu, China
May 16, 2009 to May 17, 2009
ISBN: 978-0-7695-3614-9
pp: 717-721
This paper investigates state estimation problem for a new class of discrete-time stochastic recurrent neural networks (RNNs) with Markov jumping parameters and time-delays. The time-delays considered in this paper are mixed and include time-varying discrete delays and distributed delays. The discrete-time neural networks have a finite number of modes, and the modes may jump from one to another according to a Markov chain. We aim at designing a state estimator to estimate the neuron state through available output measurements. By using Laypunov-Krasovskii functional and linear matrix inequality (LMI) approach, a sufficient condition is established to solve the state estimation problem. The desired estimator matrix gain is characterized in terms of the solution to these LMIs. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design method.
time neural networks; state estimation; Markov jumping systems; mixed time-delays; LMI

L. Gao and H. Chu, "State Estimation for Discrete-Time Markov Jumping Stochastic Neural Networks with Mixed Time-Delays," 2009 Pacific-Asia Conference on Circuits, Communications and Systems (PACCS 2009)(PACCS), Chengdu, 2009, pp. 717-721.
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