EconPapers    
Economics at your fingertips  
 

Hybrid projective synchronization of complex-valued memristive neural networks via concise prescribed-time control strategies

Hao Pu, Fengjun Li, Qingyun Wang and Jie Ran

Physica A: Statistical Mechanics and its Applications, 2025, vol. 665, issue C

Abstract: This article aims to consider the prescribed-time hybrid projective synchronization of fully complex-valued memristive delayed neural networks with discontinuous activation. Above all, a new prescribed-time stability lemma is established, of which the settling time is directly a parameter of the auxiliary function and the conservatism of the conditions is reduced. Unlike common research methods, to simplify the operation, the complex-valued memristive neural network model is converted into one with uncertain parameters in view of the convex analysis approach. Subsequently, applying Filippov’s solution theory, prescribed-time stability theory, inequality techniques, and non-separation method, several novel and concise sufficient criteria are established to ensure the considered systems achieve prescribed-time synchronization by designing some controllers. Additionally, unlike common power-law type prescribed-time controllers, the ones designed in this paper are simpler because they do not involve sign function and time-delay term and have relatively fewer terms. Especially, one of their control gains is a time variable rather than a constant. And the prescribed synchronization time is independent of any initial values and parameters of the system, and can be preset arbitrarily according to actual needs. Compared with existing works, the hybrid projective coefficients of this paper are complex-valued that can be adjusted rather than real-valued, and projective synchronization, complete synchronization and anti-synchronization are its special cases. Eventually, numerical simulation results are furnished to manifest the effectiveness of the acquired theoretical outcomes.

Keywords: Prescribed-time synchronization; Complex-valued memristive neural networks; Hybrid projective; Simple control strategies; Non-separation method (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437125001177
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:phsmap:v:665:y:2025:i:c:s0378437125001177

DOI: 10.1016/j.physa.2025.130465

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-25
Handle: RePEc:eee:phsmap:v:665:y:2025:i:c:s0378437125001177