Event-triggered state estimation for a class of delayed recurrent neural networks with sampled-data information
type: Item
instance of: scholarly article
title: Event-triggered state estimation for a class of delayed recurrent neural networks with sampled-data information
zbMATH Open document ID: 1255.62305
DOI: 10.1155/2012/731453
author: Hongjie Li
published in: Abstract and Applied Analysis
publication date: 2012-12-18T00:00:00Z
review text: Summary: 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 standard numerical software. Finally, a numerical example is provided to show the effectiveness of the proposed event-triggered scheme.
Mathematics Subject Classification ID: 62M45
Mathematics Subject Classification ID: 65C60
zbMATH DE Number: 6116440
zbMATH Keywords: state estimation problem
zbMATH Keywords: recurrent neural networks
zbMATH Keywords: sampled-data information
zbMATH Keywords: time-varying delays
Wikidata QID: Q58696683
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/731453
OpenAlex ID: W2149844794
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description: scientific article; zbMATH DE number 6116440