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Extended dissipative output‐feedback control for discrete‐time bidirectional associative memory neural networks via delay‐partitioning approach

Extended dissipative output‐feedback control for discrete‐time bidirectional associative memory... This paper is concerned with the analysis of an extended dissipativity performance for a class of bidirectional associative memory (BAM) neural networks (NNs) having time‐varying delays. To achieve this, the idea of the delay‐partitioning approach is used, where the range of time‐varying delay factors is partitioned into a finite number of equidistant subintervals. A delay‐partitioning based Lyapunov–Krasovskii function is introduced on these intervals, and some new delay‐dependent extended dissipativity results are established in terms of linear matrix inequalities, which also depend on the partition size of the delay factor. Further, numerical examples are performed to acknowledge the extended dissipativity performance of delayed discrete‐time BAM NN; further, four case studies were explored with their simulations to validate the impact of the delay‐partitioning approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Control Wiley

Extended dissipative output‐feedback control for discrete‐time bidirectional associative memory neural networks via delay‐partitioning approach

Adhira, B.; Nagamani, G.
Asian Journal of Control , Volume Early View – Mar 14, 2023
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16 pages

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Publisher
Wiley
Copyright
© 2023 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd
ISSN
1561-8625
eISSN
1934-6093
DOI
10.1002/asjc.3059
Publisher site
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Abstract

This paper is concerned with the analysis of an extended dissipativity performance for a class of bidirectional associative memory (BAM) neural networks (NNs) having time‐varying delays. To achieve this, the idea of the delay‐partitioning approach is used, where the range of time‐varying delay factors is partitioned into a finite number of equidistant subintervals. A delay‐partitioning based Lyapunov–Krasovskii function is introduced on these intervals, and some new delay‐dependent extended dissipativity results are established in terms of linear matrix inequalities, which also depend on the partition size of the delay factor. Further, numerical examples are performed to acknowledge the extended dissipativity performance of delayed discrete‐time BAM NN; further, four case studies were explored with their simulations to validate the impact of the delay‐partitioning approach.

Journal

Asian Journal of Control – Wiley

Published: Mar 14, 2023

Keywords: delay‐partitioning approach; discrete‐time neural networks; extended dissipativity performance; linear matrix inequality; Lyapunov–Krasovskii functional

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