Globally asymptotic synchronization for complex-valued BAM neural networks by the differential inequality way

Dazhao Chen and Zhengqiu Zhang

Chaos, Solitons & Fractals, 2022, vol. 164, issue C

Abstract: The globally asymptotic synchronization (GAS) topic for the master–slave complex-valued (CV) BAM neural networks (NNS) is approached. Giving up adopting matrix measure way, linear matrix inequality (LMI) means and integral inequality way, by adopting the new study method: the differential inequality way, we get two new criteria guaranteeing that the master CVBAM NNS and the response NNS can reach the GAS. In applying the differential inequality way, the obtaining of two local extremum points and the application of the properties of higher order polynomial are essentially skilful and the results obtained are sufficiently novel. Hence, our study is of important meaning in the study of FTS of NNS.

Keywords: Master–slave CVBAMNNS; GAS; The skilfully differential inequality; Two local extremum points; The properties of high order polynomial (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077922008608
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:chsofr:v:164:y:2022:i:c:s0960077922008608

DOI: 10.1016/j.chaos.2022.112681

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().