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Delay‐dependent state estimation for discrete Markovian jump neural networks with time‐varying delay
Author(s) -
Wu Zhengguang,
Shi Peng,
Su Hongye,
Chu Jian
Publication year - 2011
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.219
Subject(s) - estimator , control theory (sociology) , discrete time and continuous time , state (computer science) , artificial neural network , state estimator , jump , mathematics , linear matrix inequality , stability (learning theory) , markov process , computer science , control (management) , mathematical optimization , algorithm , statistics , artificial intelligence , physics , quantum mechanics , machine learning
The state estimation problem is discussed for discrete Markovian jump neural networks with time‐varying delays in terms of linear matrix inequality (LMI) approach. The considered transition probabilities are assumed to be time‐variant and partially unknown. The aim of the state estimation problem is to design a state estimator to estimate the neuron states and ensure the stochastic stability of the error‐state system. A delay‐dependent sufficient condition for the existence of the desired state estimator is proposed. An explicit expression of the desired estimator is also given. A numerical example is introduced to show the effectiveness of the given result.Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society