Open AccessDesign of exponential state estimators for neutral-type neural networks with mixed time delays
Author(s) -
Bo Du
Publication year - 2016
Publication title -
filomat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 34
eISSN - 2406-0933
pISSN - 0354-5180
DOI - 10.2298/fil1613435d
Subject(s) - estimator , mathematics , control theory (sociology) , linear matrix inequality , exponential stability , state (computer science) , state estimator , artificial neural network , stability (learning theory) , type (biology) , class (philosophy) , matrix (chemical analysis) , mathematical optimization , algorithm , computer science , control (management) , nonlinear system , statistics , artificial intelligence , machine learning , ecology , physics , materials science , quantum mechanics , composite material , biology
In this paper, the state estimation problem is dealt with for a class of neutral-type neural networks with mixed time delays. We aim at designing a state estimator to estimate the neuron states, through available output measurements, such that the dynamics of the estimation error is globally exponentially stable in the presence of mixed time delays. By using the Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to guarantee the existence of the state estimators. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.