Convergence of Neutral Type Fuzzy Cellular Neural Networks with D Operator

Convergence of Neutral Type Fuzzy Cellular Neural Networks with D Operator Neural Process Lett https://doi.org/10.1007/s11063-018-9864-y Convergence of Neutral Type Fuzzy Cellular Neural Networks with D Operator Zhibin Chen © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract A model of neutral type fuzzy cellular neural networks with D operator is pro- posed. 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. Keywords Exponential convergence · Fuzzy cellular neural network · Neutral type delay · D operator Mathematics Subject Classification 34C25 · 34K13 1 Introduction Over past twenty years, the successful applications of delayed fuzzy cellular neural network (FCNN) have been witnessed in many areas [1–5] including combinatorial optimization, pat- tern recognition, associative memories, image processing, and signal processing. Because of the complex neural reactions, it is natural and significant that some information about the derivative of the past state should be included [6–10]. In particular, the stability and other dynamic behaviors for different classes of FCNNs with neutral type delays were studied in [11–15]. It should be pointed out that all neutral type FCNNs models considered in the above mentioned 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

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Complex Systems; Computational Intelligence
ISSN
1370-4621
eISSN
1573-773X
D.O.I.
10.1007/s11063-018-9864-y
Publisher site
See Article on Publisher Site

Abstract

Neural Process Lett https://doi.org/10.1007/s11063-018-9864-y Convergence of Neutral Type Fuzzy Cellular Neural Networks with D Operator Zhibin Chen © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract A model of neutral type fuzzy cellular neural networks with D operator is pro- posed. 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. Keywords Exponential convergence · Fuzzy cellular neural network · Neutral type delay · D operator Mathematics Subject Classification 34C25 · 34K13 1 Introduction Over past twenty years, the successful applications of delayed fuzzy cellular neural network (FCNN) have been witnessed in many areas [1–5] including combinatorial optimization, pat- tern recognition, associative memories, image processing, and signal processing. Because of the complex neural reactions, it is natural and significant that some information about the derivative of the past state should be included [6–10]. In particular, the stability and other dynamic behaviors for different classes of FCNNs with neutral type delays were studied in [11–15]. It should be pointed out that all neutral type FCNNs models considered in the above mentioned

Journal

Neural Processing LettersSpringer Journals

Published: May 30, 2018

References

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