Paper Info
Title
State-Feedback Filtering for Delayed Discrete-Time Complex-Valued Neural Networks
Abstract
This article explores a new filtering problem for the class of delayed discrete-time complex-valued neural networks (CVNNs) via state-feedback control design. The novelty of this article comes from the consideration of the newly developed complex-valued reciprocal convex matrix inequality as well as the complex-valued Jensen-based summation inequalities (JSIs). By employing an appropriate Lyapunov-Krasovskii functional (LKF) and by using newly proposed complex-valued inequalities, attention is concentrated on the design of a state-feedback filter such that the associated filtering error system is asymptotically stable with prescribed filter and control gain matrices. The proposed theoretical results are presented in terms of complex-valued linear matrix inequalities (LMIs) that can be solved numerically by using the YALMIP toolbox in MATLAB software. Additionally, one numerical example is given to confirm the validity of the resulting sufficient conditions with the availability of the suitable control and filter design.
Year
DOI
Venue
2020
10.1109/TNNLS.2019.2957304
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
Complex-valued summation inequality,discrete-time complex-valued neural networks (CVNNs),linear matrix inequality (LMI),Lyapunov–Krasovskii functional (LKF),state-feedback control
Journal
31
Issue
ISSN
Citations 
11
2162-237X
1
PageRank 
References 
Authors
0.35
14
2
Name
Order
Citations
PageRank
G. Soundara Rajan172.44
g nagamani2628.08