Paper Info
Title
Delay-Dependent And Independent State Estimation For Bam Cellular Neural Networks With Multi-Proportional Delays
Abstract
This paper deals with the issue of state estimation for the class of bidirectional associative memory cellular neural networks (BAMCNNs) involving multi-proportional delays. The main objective of this problem is to sketch a state estimator by utilizing the known output measurements of the proposed network in such a way that the dynamics of the estimation error system is globally asymptotically stable. By formulating a proper Lyapunov-Krasovskii functional (LKF) and making use of the Lyapunov stability theory, delay-dependent and independent sufficient conditions are obtained in the form of linear matrix inequalities (LMIs) to achieve the prescribed estimation performance. By using specified parameter values, the state estimator gain matrices are calculated by means of solving the obtained LMIs. Finally, numerical illustrations are explored to show the applicability and advantages of the proposed theoretical results.
Year
DOI
Venue
2021
10.1007/s00034-020-01622-4
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
Keywords
DocType
Volume
State estimation, BAM cellular neural networks, Proportional delay, Lyapunov-Krasovskii functional, Linear matrix inequality
Journal
40
Issue
ISSN
Citations 
7
0278-081X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
G. Nagamani100.34
A. Karnan200.34
G. Soundara Rajan372.44