Sambas Aceng, Zhang Xuncai, Moghrabi Issam A R, Vaidyanathan Sundarapandian, Benkouider Khaled, Alçın Murat, Koyuncu İsmail, Tuna Murat, Sulaiman Ibrahim M, Mohamed Mohamad Afendee, Johansyah Muhamad Deni
Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Kampung Gong Badak, 21300, Kuala Terengganu, Malaysia.
Department of Mechanical Engineering, Universitas Muhammadiyah Tasikmalaya, Tasikmalaya, 46196, Indonesia.
Sci Rep. 2024 Nov 28;14(1):29602. doi: 10.1038/s41598-024-80969-z.
In this paper, we introduce a category of Novel Jerk Chaotic (NJC) oscillators featuring symmetrical attractors. The proposed jerk chaotic system has three equilibrium points. We show that these equilibrium points are saddle-foci points and unstable. We have used traditional methods such as bifurcation diagrams, phase portraits, and Lyapunov exponents to analyze the dynamic properties of the proposed novel jerk chaotic system. Moreover, simulation results using Multisim, based on an appropriate electronic implementation, align with the theoretical investigations. Additionally, the NJC system is solved numerically using the Dormand Prince algorithm. Subsequently, the Jerk Chaotic System is modeled using a multilayer Feed-Forward Neural Network (FFNN), leveraging its nonlinear mapping capability. This involved utilizing 20,000 values of x, x, and x for training (70%), validation (15%), and testing (15%) processes, with the target values being their iterative values. Various network structures were experimented with, and the most suitable structure was identified. Lastly, a chaos-based image encryption algorithm is introduced, incorporating scrambling technique derived from a dynamic DNA coding and an improved Hilbert curve. Experimental simulations confirm the algorithm's efficacy in enduring numerous attacks, guaranteeing strong resiliency and robustness.
在本文中，我们介绍了一类具有对称吸引子的新型急动混沌（NJC）振荡器。所提出的急动混沌系统有三个平衡点。我们表明这些平衡点是鞍焦点且不稳定。我们使用了诸如分岔图、相图和李雅普诺夫指数等传统方法来分析所提出的新型急动混沌系统的动力学特性。此外，基于适当的电子实现，使用Multisim进行的仿真结果与理论研究一致。另外，使用多曼德 - 普林斯算法对NJC系统进行了数值求解。随后，利用多层前馈神经网络（FFNN）的非线性映射能力对急动混沌系统进行建模。这涉及使用(x)、(x)和(x)的20,000个值进行训练（70%）、验证（15%）和测试（15%）过程，并将目标值设为它们的迭代值。试验了各种网络结构，并确定了最合适的结构。最后，引入了一种基于混沌的图像加密算法，该算法结合了从动态DNA编码导出的置乱技术和改进的希尔伯特曲线。实验仿真证实了该算法在经受众多攻击方面的有效性，确保了强大的弹性和鲁棒性。
