Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6865922 | Neurocomputing | 2015 | 18 Pages |
Abstract
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Deyin Yao, Qing Lu, Chengwei Wu, Ziran Chen,