Journal article
Sampled-Data Synchronization Analysis of Markovian Neural Networks With Generally Incomplete Transition Rates
IEEE transaction on neural networks and learning systems, v 28(3), pp 740-752
01 Mar 2017
PMID: 26731780
Abstract
This paper investigates the problem of sampled-data synchronization for Markovian neural networks with generally incomplete transition rates. Different from traditional Markovian neural networks, each transition rate can be completely unknown or only its estimate value is known in this paper. Compared with most of existing Markovian neural networks, our model is more practical because the transition rates in Markovian processes are difficult to precisely acquire due to the limitations of equipment and the influence of uncertain factors. In addition, the time-dependent Lyapunov-Krasovskii functional is proposed to synchronize drive system and response system. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilize the upper bound of variable sampling interval and the sawtooth structure information of varying input delay. Moreover, the desired sampled-data controllers are obtained. Finally, two examples are provided to illustrate the effectiveness of the proposed method.
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Details
- Title
- Sampled-Data Synchronization Analysis of Markovian Neural Networks With Generally Incomplete Transition Rates
- 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 28(3), pp 740-752
- Publisher
- IEEE
- Number of pages
- 13
- Grant note
- Development Project of Key Laboratory of Liaoning province 2013ZCX14 / IAPI Fundamental Research Funds 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:000395980500022
- Scopus ID
- 2-s2.0-84953257318
- Other Identifier
- 991019320404404721
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Hardware & Architecture
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic