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Convergence of Neutral Type Fuzzy Cellular Neural Networks with D Operator

Convergence of Neutral Type Fuzzy Cellular Neural Networks with D Operator A model of neutral type fuzzy cellular neural networks with D operator is proposed. Applying differential inequality techniques, several sufficient conditions are derived to ensure the global exponential convergence of solutions for the proposed neural networks. Finally, a numerical simulation example is given to illustrate the effectiveness of the obtained results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Processing Letters Springer Journals

Convergence of Neutral Type Fuzzy Cellular Neural Networks with D Operator

Neural Processing Letters , Volume 49 (3) – May 30, 2018

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Artificial Intelligence; Complex Systems; Computational Intelligence
ISSN
1370-4621
eISSN
1573-773X
DOI
10.1007/s11063-018-9864-y
Publisher site
See Article on Publisher Site

Abstract

A model of neutral type fuzzy cellular neural networks with D operator is proposed. Applying differential inequality techniques, several sufficient conditions are derived to ensure the global exponential convergence of solutions for the proposed neural networks. Finally, a numerical simulation example is given to illustrate the effectiveness of the obtained results.

Journal

Neural Processing LettersSpringer Journals

Published: May 30, 2018

References