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Observer design for neutral‐type neural networks with discrete and distributed time‐varying delays
Author(s) -
Dong Yali,
Chen Laijun,
Mei Shengwei
Publication year - 2019
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2970
Subject(s) - control theory (sociology) , observer (physics) , estimator , artificial neural network , nonlinear system , matlab , type (biology) , computer science , interval (graph theory) , class (philosophy) , exponential stability , mathematics , state (computer science) , state estimator , matrix (chemical analysis) , discrete time and continuous time , control (management) , algorithm , artificial intelligence , ecology , statistics , physics , materials science , quantum mechanics , combinatorics , composite material , biology , operating system
Summary This paper is concerned with the problem of state estimation for a class of neural networks with discrete and distributed interval time‐varying delays. We propose a new approach of nonlinear estimator design for the class of neutral‐type neural networks. By constructing a newly augmented Lyapunov‐Krasovskii functional, we establish sufficient conditions to guarantee the estimation error dynamics to be globally exponentially stable. The obtained results are formulated in terms of linear matrix inequalities (LMIs), which can be easily verified by the MATLAB LMI control toolbox. Then, the desired estimators gain matrix is characterized in terms of the solution to these LMIs. Three numerical examples are given to show the effectiveness of the proposed design method.