Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities

Feng Li, Shuai Song, Jianrong Zhao, Shengyuan Xu and Zhengqiang Zhang

Applied Mathematics and Computation, 2019, vol. 360, issue C, 1-13

Abstract: This paper pays attention to the synchronization control issue for Markov jump neural networks with partial information on system modes (or called Markov states), which leads to the case that the system modes cannot be directly accessed. An hidden Markov model (HMM)-based detector with partially known detection probabilities is employed to detect the system modes. With the help of the HMM and an activation function dividing method, a less conservative controller design technique is established. The designed HMM-based controller can be converted to mode-independent/-dependent one by suitably adjusting some design parameters. Finally, the availability of the established HMM-based controller design technique is verified by an illustrative example.

Keywords: Markov jump neural networks; Synchronization control; Hidden Markov model (HMM); Partial detection probabilities (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300319303145
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:360:y:2019:i:c:p:1-13

DOI: 10.1016/j.amc.2019.04.032

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().