Open Access
2012 Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information
Hongjie Li
Abstr. Appl. Anal. 2012(SI04): 1-21 (2012). DOI: 10.1155/2012/731453


The paper investigates the state estimation problem for a class of recurrent neural networks with sampled-data information and time-varying delays. The main purpose is to estimate the neuron states through output sampled measurement; a novel event-triggered scheme is proposed, which can lead to a significant reduction of the information communication burden in the network; the feature of this scheme is that whether or not the sampled data should be transmitted is determined by the current sampled data and the error between the current sampled data and the latest transmitted data. By using a delayed-input approach, the error dynamic system is equivalent to a dynamic system with two different time-varying delays. Based on the Lyapunov-krasovskii functional approach, a state estimator of the considered neural networks can be achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Finally, a numerical example is provided to show the effectiveness of the proposed event-triggered scheme.


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Hongjie Li. "Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information." Abstr. Appl. Anal. 2012 (SI04) 1 - 21, 2012.


Published: 2012
First available in Project Euclid: 5 April 2013

zbMATH: 1255.62305
MathSciNet: MR2975274
Digital Object Identifier: 10.1155/2012/731453

Rights: Copyright © 2012 Hindawi

Vol.2012 • No. SI04 • 2012
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