Paper Info
Title
Event-Triggered State Estimation For Markovian Jumping Impulsive Neural Networks With Interval Time-Varying Delays
Abstract
This paper investigates the event-triggered state estimation problem of Markovian jumping impulsive neural networks with interval time-varying delays. The purpose is to design a state estimator to estimate system states through available output measurements. In the neural networks, there are a set of modes, which are determined by Markov chain. A Markovian jumping time-delay impulsive neural networks model is employed to describe the event-triggered scheme and the network- related behaviour, such as transmission delay, data package dropout and disorder. The proposed event-triggered scheme is used to determine whether the sampled state information should be transmitted. The discrete delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. First, we design a state observer to estimate the neuron states. Second, based on a novel Lyapunov-Krasovskii functional (LKF) with triple-integral terms and using an improved inequality, several sufficient conditions are derived. The derived conditions are formulated in terms of a set of linear matrix inequalities , under which the estimation error system is globally asymptotically stable in the mean square sense. Finally, numerical examples are given to show the effectiveness and superiority of the results.
Year
DOI
Venue
2019
10.1080/00207179.2017.1350884
INTERNATIONAL JOURNAL OF CONTROL
Keywords
Field
DocType
Event-triggered, markovian jumping parameters, Lyapunov-Krasovskii functional, linear matrix inequality, state estimation
State observer,Mathematical optimization,State information,Control theory,Markov chain,Transmission delay,Event triggered,Markovian jumping,Artificial neural network,Mathematics,Linear matrix inequality
Journal
Volume
Issue
ISSN
92
2
0020-7179
Citations 
PageRank 
References 
3
0.37
36
Authors
3
Name
Order
Citations
PageRank
M. Syed Ali151839.49
R. Vadivel2101.79
R. Saravanakumar323112.70