Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Passivity Analysis of Stochastic Memristor-Based Complex-Valued Recurrent Neural Networks with Mixed Time-Varying Delays

Passivity Analysis of Stochastic Memristor-Based Complex-Valued Recurrent Neural Networks with... In this paper, the passivity analysis of stochastic memristor-based complex-valued recurrent neural networks (SMCVRNNs) with discrete and distributed time-varying delays is conducted. We adopt a switched system to describe the SMCVRNN with mixed time-varying delays. Appropriate Lyapunov–Krasovski functionals are constructed to analyze the passivity of SMCVRNNs under consideration. Two sufficient conditions are presented in terms of linear matrix inequalities which assure that the SMCVRNNs are stochastically passive. The effectiveness of the obtained results is demonstrated by two examples. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Processing Letters Springer Journals

Passivity Analysis of Stochastic Memristor-Based Complex-Valued Recurrent Neural Networks with Mixed Time-Varying Delays

Loading next page...
 
/lp/springer_journal/passivity-analysis-of-stochastic-memristor-based-complex-valued-z12bAWwov6

References (37)

Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Complex Systems; Computational Intelligence
ISSN
1370-4621
eISSN
1573-773X
DOI
10.1007/s11063-017-9687-2
Publisher site
See Article on Publisher Site

Abstract

In this paper, the passivity analysis of stochastic memristor-based complex-valued recurrent neural networks (SMCVRNNs) with discrete and distributed time-varying delays is conducted. We adopt a switched system to describe the SMCVRNN with mixed time-varying delays. Appropriate Lyapunov–Krasovski functionals are constructed to analyze the passivity of SMCVRNNs under consideration. Two sufficient conditions are presented in terms of linear matrix inequalities which assure that the SMCVRNNs are stochastically passive. The effectiveness of the obtained results is demonstrated by two examples.

Journal

Neural Processing LettersSpringer Journals

Published: Aug 11, 2017

There are no references for this article.