A complex-valued time varying zeroing neural network model for synchronization of complex chaotic systems

Jin, Jie; Fang, Jing; Chen, Chaoyang; Li, Zhijing; Yu, Fei

doi:10.1007/s11071-024-10528-8

Nonlinear Dyn

2024

DOI: 10.1007/s11071-024-10528-8

|View full text |Cite

A complex-valued time varying zeroing neural network model for synchronization of complex chaotic systems

Jie Jin,

Jing Fang,

Chaoyang Chen

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Dynamic Hill Cipher with Arnold Scrambling Technique for Medical Images Encryption

Xi,

Ning,

Jin

et al. 2024

Mathematics

View full text Add to dashboard Cite

Cryptography is one of the most important branches of information security. Cryptography ensures secure communication and data privacy, and it has been increasingly applied in healthcare and related areas. As a significant cryptographic method, the Hill cipher has attracted significant attention from experts and scholars. To enhance the security of the traditional Hill cipher (THC) and expand its application in medical image encryption, a novel dynamic Hill cipher with Arnold scrambling technique (DHCAST) is proposed in this work. Unlike the THC, the proposed DHCAST uses a time-varying matrix as its secret key, which greatly increases the security of the THC, and the new DHCAST is successfully applied in medical images encryption. In addition, the new DHCAST method employs the Zeroing Neural Network (ZNN) in its decryption to find the time-varying inversion key matrix (TVIKM). In order to enhance the efficiency of the ZNN for solving the TVIKM, a new fuzzy zeroing neural network (NFZNN) model is constructed, and the convergence and robustness of the NFZNN model are validated by both theoretical analysis and experiment results. Simulation experiments show that the convergence time of the NFZNN model is about 0.05 s, while the convergence time of the traditional Zeroing Neural Network (TZNN) model is about 2 s, which means that the convergence speed of the NFZNN model is about 400 times that of the TZNN model. Moreover, the Peak Signal to Noise Ratio (PSNR) and Number of Pixel Change Rate (NPCR) of the proposed DHCAST algorithm reach 9.51 and 99.74%, respectively, which effectively validates its excellent encryption quality and attack prevention ability.

show abstract

A Dynamic Hill Cipher with Arnold Scrambling Technique for Medical Images Encryption

Xi,

Ning,

Jin

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A complex-valued time varying zeroing neural network model for synchronization of complex chaotic systems

Cited by 1 publication

References 35 publications

A Dynamic Hill Cipher with Arnold Scrambling Technique for Medical Images Encryption

A Dynamic Hill Cipher with Arnold Scrambling Technique for Medical Images Encryption

Contact Info

Product

Resources

About