SYNCHRONIZATION OF DELAYED INTEGER ORDER AND DELAYED FRACTIONAL ORDER RECURRENT NEURAL NETWORKS SYSTEM WITH ACTIVE SLIDING MODE CONTROL

被引:0
|
作者
Abd Latiff, Fatin Nabila [1 ]
Othman, Wan Ainun Mior [1 ]
Kumaresan, N. [1 ]
机构
[1] Univ Malaya, Fac Sci, Inst Math Sci, Kuala Lumpur 50603, Malaysia
关键词
Chaotic synchronization; Double encryption; Chaotic Neural Networks (CNNs); Sliding surface; RSA encryption; Cybersecurity; Cryptography technology; CHAOTIC SYSTEMS;
D O I
10.14456/ITJEMAST.2020.229
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Chaotic Neural Networks (CNNs) has been gaining a lot of attention and have become a hot topic from researchers with good expectation. To resolve the synchronization's problem of delayed integer order recurrent neural networks (IoDRNNASM) and delayed fractional-order recurrent neural networks (FoDRNNASM), an active sliding mode control (ASMC) scheme is introduced. Factional Lyapunov direct methodology (FLDM) is designed and is enforced to ASMC of the systems to keep the stability of the systems. To investigate the characteristics of IoDRNNASM and FoDRNNASM, we tend to enforce the method of numerical simulation by utilizing MATLAB programming to demonstrate the performance and efficiency of the results. Based on this study, the results show that the synchronization between integer-order and fractional-order will significantly occur once the recommended ASMC is introduced. This main result can provide a great advantage within the area of network security of secure communication by implement double encryption by conducting RSA encryption. We do believe that this idea can improve security and provides strong protection in secure communications. Disciplinary: Mathematics, Computer Science (Network/Cyber Security). 2020 INT TRANS J ENG MANAG SCI TECH.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] Comparative exploration on bifurcation behavior for integer-order and fractional-order delayed BAM neural networks
    Xu, Changjin
    Mu, Dan
    Liu, Zixin
    Pang, Yicheng
    Liao, Maoxin
    Li, Peiluan
    Yao, Lingyun
    Qin, Qiwen
    NONLINEAR ANALYSIS-MODELLING AND CONTROL, 2022, 27 (06): : 1030 - 1053
  • [32] Comparative exploration on bifurcation behavior for integer-order and fractional-order delayed BAM neural networks
    Xu, Changjin
    Mu, Dan
    Liu, Zixin
    Pang, Yicheng
    Liao, Maoxin
    Li, Peiluan
    Yao, Lingyun
    Qin, Qiwen
    NONLINEAR ANALYSIS-MODELLING AND CONTROL, 2022, 27 (01):
  • [33] Projective synchronization of fractional-order delayed neural networks based on the comparison principle
    Weiwei Zhang
    Jinde Cao
    Ranchao Wu
    Ahmed Alsaedi
    Fuad E. Alsaadi
    Advances in Difference Equations, 2018
  • [34] Synchronization analysis for delayed spatio-temporal neural networks with fractional-order
    Zheng, Bibo
    Hu, Cheng
    Yu, Juan
    Jiang, Haijun
    NEUROCOMPUTING, 2021, 441 : 226 - 236
  • [35] Stability and synchronization of memristor-based fractional-order delayed neural networks
    Chen, Liping
    Wu, Ranchao
    Cao, Jinde
    Liu, Jia-Bao
    NEURAL NETWORKS, 2015, 71 : 37 - 44
  • [36] Projective synchronization of fractional-order delayed neural networks based on the comparison principle
    Zhang, Weiwei
    Cao, Jinde
    Wu, Ranchao
    Alsaedi, Ahmed
    Alsaadi, Fuad E.
    ADVANCES IN DIFFERENCE EQUATIONS, 2018,
  • [37] Adaptive quaternion projective synchronization of fractional order delayed neural networks in quaternion field
    Zhang, Weiwei
    Sha, Chunlin
    Cao, Jinde
    Wang, Guanglan
    Wang, Yuan
    APPLIED MATHEMATICS AND COMPUTATION, 2021, 400
  • [38] New Approach to Quasi-Synchronization of Fractional-Order Delayed Neural Networks
    Zhang, Shilong
    Du, Feifei
    Chen, Diyi
    FRACTAL AND FRACTIONAL, 2023, 7 (11)
  • [39] Synchronization of fractional order time delayed neural networks using matrix measure approach
    Jose, S.
    Parthiban, V.
    EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 2024,
  • [40] Event-based delayed impulsive control for fractional-order dynamic systems with application to synchronization of fractional-order neural networks
    Zheng, Bibo
    Wang, Zhanshan
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (27): : 20241 - 20251