Journal article
Mode-Dependent Stochastic Synchronization for Markovian Coupled Neural Networks With Time-Varying Mode-Delays
IEEE transaction on neural networks and learning systems, v 26(11), pp 2621-2634
01 Nov 2015
PMID: 25616083
Abstract
This paper investigates the stochastic synchronization problem for Markovian hybrid coupled neural networks with interval time-varying mode-delays and random coupling strengths. The coupling strengths are mutually independent random variables and the coupling configuration matrices are nonsymmetric. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is proposed, where some terms involving triple or quadruple integrals are considered, which makes the LKF matrices mode-dependent as much as possible. This gives significant improvement in the synchronization criteria, i.e., less conservative results can be obtained. In addition, by applying an extended Jensen's integral inequality and the properties of random variables, new delay-dependent synchronization criteria are derived. The obtained criteria depend not only on upper and lower bounds of mode-delays but also on mathematical expectations and variances of the random coupling strengths. Finally, two numerical examples are provided to demonstrate the feasibility of the proposed results.
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Details
- Title
- Mode-Dependent Stochastic Synchronization for Markovian Coupled Neural Networks With Time-Varying Mode-Delays
- Creators
- Huaguang Zhang - Northeastern UniversityJunyi Wang - Northeastern UniversityZhanshan Wang - Northeastern UniversityHongjing Liang - Northeastern University
- Publication Details
- IEEE transaction on neural networks and learning systems, v 26(11), pp 2621-2634
- Publisher
- IEEE
- Number of pages
- 14
- Grant note
- 2013ZCX14 / IAPI Fundamental Research Funds Development Project through Key Laboratory of Liaoning Province 61433004; 61473070 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) 2012AA040104 / National High Technology Research and Development Program of China
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000363242800002
- Scopus ID
- 2-s2.0-84945957378
- Other Identifier
- 991019320715504721
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Hardware & Architecture
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic