Article
Physics, Multidisciplinary
Sheng-Hao Jia, Yu-Xia Li, Qing-Yu Shi, Xia Huang
Summary: A novel memristor-based multi-scroll hyperchaotic system is proposed, and a new method for generating multi-scroll hyperchaotic attractors using a voltage-controlled memristor and a modulating sine nonlinear function is introduced. Experimental results show that the system can generate multiple coexisting hyperchaotic attractors with different topological structures.
Article
Computer Science, Interdisciplinary Applications
Shaohui Yan, Binxian Gu, Ertong Wang, Yu Ren
Summary: In this paper, an image algorithm based on multi-scroll hyperchaotic system and finite time synchronization is proposed. A novel ring-cutting scrambling method is used to scramble the images in a relatively simple way. The encryption algorithm is developed by employing plane diffusion. Particularly, the bit-plane algorithm requires restoration of four bit-planes to obtain the plaintext, which significantly reduces the deciphering ability. Experimental results demonstrate that the proposed algorithm exhibits improved resistance against violent attacks and cropping attacks through histogram, correlation coefficient red, and robustness tests.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Review
Engineering, Mechanical
Minglin Ma, Yang Yang, Zhicheng Qiu, Yuexi Peng, Yichuang Sun, Zhijun Li, Mengjiao Wang
Summary: This paper introduces a locally active discrete memristor model for the first time and analyzes its dynamical behaviors using various methods. The results show that applying the locally active discrete memristor significantly improves the chaotic properties of the map and demonstrates the existence of attractors.
NONLINEAR DYNAMICS
(2022)
Article
Optics
Yang Yang, Lidan Wang, Shukai Duan, Li Luo
Summary: This study introduces a new four-dimensional memristive hyperchaotic system and designs an image encryption algorithm based on the chaotic sequence generated by this system. Experimental results demonstrate that the algorithm has excellent encryption performance.
OPTICS AND LASER TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Jie Zhang, Yan Guo, Longhao Xu, Xiaopeng Zhu, Jing Yang
Summary: In this study, a self-replicating chaotic system based on memristor was obtained by introducing magnetron memristors model and sinusoidal functions into a simple third-order memristor chaotic system. The experimental results confirmed the feasibility of the constructed system and its high security performance in image encryption.
MICROELECTRONIC ENGINEERING
(2022)
Article
Optics
Yuexi Peng, Zixin Lan, Kehui Sun, Wei Xu
Summary: Chaotic maps are commonly used in image encryption, but many of them have limited chaotic ranges. To address this issue, a new chaotic map is proposed by combining modular sinusoidal discrete memristor and classical sine map. An image encryption algorithm is developed based on this new chaotic map, which allows controlling the encryption time by adjusting the block size. The algorithm consists of random-cross permutation and alternate-plane diffusion, which shuffle the pixels and process the image using chaotic sequences, respectively. Simulation experiments and performance analysis demonstrate the effectiveness and reliability of the proposed encryption algorithm.
OPTICS AND LASER TECHNOLOGY
(2023)
Article
Mathematics, Interdisciplinary Applications
Chunbo Xiu, Jingyao Fang, Xin Ma
Summary: In this paper, a sixth-order memristive hyperchaotic system is designed to enhance the order and Kolmogorov entropy, and improve the realizability of the circuit. The dynamic characteristics of the system are analyzed through Lyapunov exponential spectrum, phase trajectory diagram, and bifurcation diagram, and the effects of system control parameters and initial state on the system dynamic behavior are explored. Equivalent circuit models of flux-controlled and charge-controlled memristors are constructed, and the hardware simulation design is completed. The experimental results demonstrate that the proposed system can exhibit various attractors and show the behavior of period doubling bifurcation entering chaos and anti-period doubling exiting chaos. The physical realizability of the hyperchaotic system is verified through hardware circuit simulation. The memristive hyperchaotic system can be applied to image encryption to enhance the confidentiality and security of images based on its strong initial value sensitivity and large K entropy.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Engineering, Mechanical
Hairong Lin, Chunhua Wang, Li Cui, Yichuang Sun, Xin Zhang, Wei Yao
Summary: In this paper, a memristive ring neural network (MRNN) with special structure and a non-ideal flux-controlled memristor is introduced to simulate the effect of external electromagnetic radiation on neurons. The chaotic dynamics of the MRNN is investigated and verified through numerical simulations and circuit experiments. Based on the characteristics of the network, a medical image encryption scheme is proposed. Performance evaluations show that the scheme has advantages compared with other chaotic systems-based cryptosystems in terms of keyspace, information entropy, and key sensitivity.
NONLINEAR DYNAMICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Chunbo Xiu, Jingyao Fang, Yuxia Liu
Summary: A novel five-dimension memristive cellular neural network hyperchaotic system is designed to enrich the dynamic characteristics of CNN and reveal the influence of memristor nonlinearity. The effects of system parameters, initial values, and noise on the dynamic behavior are studied, providing criteria for parameter selection and verifying the physical realizability of chaotic characteristics. Additionally, a secure communication application example based on the hyperchaotic system is presented.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Engineering, Electrical & Electronic
Muslum Gur, Funda Akar, Kamil Orman, Yunus Babacan, Abdullah Yesil, Fatih Gul
Summary: In this study, a simple multioutput operational transconductance amplifier (MO-OTA)-based fully floating and electronically controllable memcapacitor emulator circuit was designed to simulate memcapacitor behavior. The circuit, composed of two MO-OTAs, two analog multipliers, two grounded passive elements, and four transistors, can be implemented in VLSI and on breadboard using discrete circuit elements. Performance analyses using TSMC 0.18 & mu;m parameters showed that the obtained results of the proposed fully floating emulator circuit are consistent with the expected memcapacitors behavior, making it suitable for an ideal memcapacitor emulator.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Hardware & Architecture
Shaohui Yan, Lin Li, Binxian Gu, Yu Cui, Jianjian Wang, Jincai Song
Summary: In this paper, a new four-dimensional multi-scroll hyperchaotic system is constructed and a new color image encryption algorithm is designed based on this system. The experimental results show that the algorithm has a high key space and is resistant to various attacks.
INTEGRATION-THE VLSI JOURNAL
(2023)
Article
Mathematics
Hanshuo Qiu, Xiangzi Zhang, Huaixiao Yue, Jizhao Liu
Summary: This paper proposes an encryption scheme based on an eighth-order hyperchaotic system, which offers a large key space and can resist various attacks, demonstrating excellent security performance.
Article
Mathematics, Interdisciplinary Applications
Qiang Lai, Liang Yang, Yuan Liu
Summary: This paper investigates discrete memristive chaotic systems with complex dynamics and constructs a hyperchaotic system with no fixed point and infinitely many coexisting attractors. It creatively introduces the iterative number as a variable to enhance the system's complexity.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Engineering, Electrical & Electronic
Qi Guo, Ning Wang, Guoshan Zhang
Summary: In this paper, a novel passive current-controlled second-order generalized memristor using simple components is presented. The model state equation and voltage-current characteristics of the memristor are studied to analyze the symmetry, dissipativity, stability, and dynamical behaviors of the circuit. The existence of various attractors is validated.
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS
(2022)
Article
Automation & Control Systems
Houzhen Li, Zhongyun Hua, Han Bao, Lei Zhu, Mo Chen, Bocheng Bao
Summary: This article introduces a discrete memristor and its coupling with discrete maps, demonstrating the characteristics of the discrete memristor and investigating its complex dynamics. The results show that the discrete memristor can enhance chaos complexity and coupling maps can generate hyperchaotic sequences.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Computer Science, Artificial Intelligence
Jianqiang Gong, Jie Jin
Summary: This paper presents a better fast convergence zeroing neural network (BFCZNN) model with a new activation function (AF) for solving dynamic nonlinear equations (DNE) and applying to control robot manipulator. The proposed BFCZNN model not only finds the solutions of DNE in fixed time, but also has better robustness than most of the previously reported studies.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Jingcan Zhu, Jie Jin, Weijie Chen, Jianqiang Gong
Summary: In this paper, a CFVZNN model with a novel CPAF and a time-varying adjustable CF is proposed for online DMI solution. The advantages of fixed-time convergence and anti-noise property of the proposed CFVZNN model are verified by strict mathematical derivation. Successful examples further validate the practical application prospects of the proposed CFVZNN model.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Computer Science, Artificial Intelligence
Jie Jin, Jingcan Zhu, Jianqing Gong, Weijie Chen
Summary: This paper explores the practicality and anti-noise ability of the zeroing neural network (ZNN) model in solving time-varying problems. Two novel activation functions are designed, and based on them, two robust ZNN models with fixed time convergence and strong noise resistance are proposed for solving time-varying Sylvester equation (TVSE). Rigorous mathematical analysis and simulation experiments validate the robustness and practicality of these two models.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Peng Zhou, Mingtao Tan, Jianbo Ji, Jie Jin
Summary: An anti-noise parameter-variable zeroing neural network (ANPVZNN) model is proposed to solve the dynamic complex matrix inversion (DCMI) problems. The model possesses fixed-time convergence and robustness, and has been successfully applied in practical applications.
Article
Computer Science, Artificial Intelligence
Jie Jin, Jingcan Zhu, Lv Zhao, Lei Chen
Summary: This paper proposes a zeroing neural network model with fixed-time convergence and noise tolerance for solving time-varying matrix inversion problems. By introducing a novel activation function, the proposed model demonstrates good performance in both theoretical analysis and experimental results.
APPLIED SOFT COMPUTING
(2022)
Article
Mathematics, Applied
Jie Jin, Weijie Chen, Lv Zhao, Long Chen, Zhijun Tang
Summary: In this paper, a new nonlinear zeroing neural network (NZNN) model is proposed to enhance the convergent speed and robustness for time-varying linear matrix equation solving. The superiority of the proposed NZNN model is theoretically validated through rigorous mathematical analysis, and its practical abilities are further verified through engineering oriented applications.
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Weijie Chen, Jie Jin, Chaoyang Chen, Fei Yu, Chunhua Wang
Summary: This paper proposes a disturbance suppression zeroing neural network (DSZNN) for robust synchronization of chaotic and hyperchaotic systems, which is further validated on FPGA hardware. The DSZNN shows faster convergent speed and higher accuracy compared to SEZNN and CZNN. The superior performance of DSZNN is demonstrated through theoretical analysis, numerical simulations, and hardware validations.
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
(2022)
Article
Computer Science, Artificial Intelligence
Jie Jin, Lv Zhao, Lei Chen, Weijie Chen
Summary: A new neural network model RZNN is proposed in this paper to solve dynamic complex matrix equations in noisy environment by introducing a new activation function (NAF). The robustness and convergence of the model are verified through numerical simulations, and it is successfully applied to manipulator trajectory tracking control.
FRONTIERS IN NEUROROBOTICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Jie Jin, Weijie Chen, Lixin Qiu, Jingcan Zhu, Haiyan Liu
Summary: This paper introduces the application of the zeroing neural network (ZNN) model in solving dynamic matrix equations, proposes a novel activation function (NAF), and verifies the fixed-time convergence and robustness to noises of the model through rigorous mathematical analysis and numerical simulation results. Two examples of electrical circuit currents computing and robotic manipulator trajectory tracking further demonstrate the practical application ability of the proposed model in noisy environment.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Weijie Chen, Jie Jin, Dimitrios Gerontitis, Lixin Qiu, Jingcan Zhu
Summary: This study proposes two novel activation functions (NAF) to improve the performance of recurrent neural network (RNN) models in text classification and dynamic problems solving. The first NAF (NAF(1)) is applied to various RNN models for text classification, achieving higher accuracy compared to traditional activation functions. Additionally, the second NAF (NAF(2)) is used to construct an improved fixed-time convergent RNN model (IFTCRNN) for solving time-varying problems, demonstrating fixed-time convergence and strong robustness to noises.
NEURAL PROCESSING LETTERS
(2023)
Article
Automation & Control Systems
Jie Jin, Jingcan Zhu, Lv Zhao, Lei Chen, Long Chen, Jianqiang Gong
Summary: The zeroing neural network (ZNN) is a classical and effective method for solving various time-varying problems, widely applied in scientific and industrial realms. Robustness and convergence are two essential criteria in evaluating the quality of the ZNN model, but the adjustability of its convergence speed has been neglected in prior works. To address this issue, a well-designed activation function (WDAF) is proposed, leading to the development of a robust predefined-time convergence ZNN (RPTCZNN) model with adjustable convergence speed. The model is validated through mathematical analysis and simulation experiments, showcasing its superior convergence and robustness in solving dynamic matrix inversion problems and enabling tracking control of robotic manipulators.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Physics, Multidisciplinary
Xiangyu Lan, Jie Jin, Haiyan Liu
Summary: The zeroing neural network (ZNN) is a powerful method for solving time-varying problems, widely used in practical applications modeled as time-varying linear matrix equations (TVLME). However, existing ZNN models often neglect noises and inequality constraints in solving TVLME problems. To address this, a non-linearly activated ZNN (NAZNN) model with a designed non-linear activation function is proposed for solving constrained TVLME (CTVLME) problems. The proposed NAZNN model is theoretically verified for convergence and robustness, while simulation and experimental results demonstrate its effectiveness in dealing with CTVLME and trajectory tracking problems.
FRONTIERS IN PHYSICS
(2023)
Article
Engineering, Electrical & Electronic
Jie Jin, Weijie Chen, Aijia Ouyang, Haiyan Liu
Summary: This paper proposes a method to improve the convergence and noise resistance ability of ZNN models by designing a fuzzy activation function, which is applied to online fast computing of circuit currents. By introducing fuzzy logic technique, the convergence and noise resistance ability of the model are further enhanced, and prescribed-time stability is achieved irrelevant to the initial states even in noisy environment. Mathematical analysis and simulation results verify the robustness and effectiveness of the proposed method for practical applications.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Automation & Control Systems
Jie Jin, Weijie Chen, Chaoyang Chen, Long Chen, Zhijun Tang, Lei Chen, Lianghong Wu, Changren Zhu
Summary: The article proposes a new pattern activation function called power piecewise activation function (PPAF) to establish a predefined fixed-time convergent zeroing neural network (PFTZNN) for solving time-varying quadratic programming problems. The PPAF's remarkable feature of multisegmentation allows flexible adjustment of its parameters according to actual needs. Detailed mathematical analysis validates the fixed-time convergence property of the PPAF-activated PFTZNN model and calculates its upper bound convergence time. Comparative simulation results demonstrate the PPAF-activated PFTZNN model's superior convergence speed and robustness compared to other existing ZNN models. The practical application ability of the proposed PFTZNN model is demonstrated through simulation experiments and real-time trajectory tracking tasks with a dual-arm manipulator.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Jie Jin, Weijie Chen, Aijia Ouyang, Fei Yu, Haiyan Liu
Summary: This paper proposes a new time-varying fuzzy parameter ZNN (TVFP-ZNN) model for achieving synchronization of chaotic systems against external noises. Compared with the other three models, the TVFP-ZNN model not only has the fastest convergence speed, but also exhibits the strongest robustness to noises. Furthermore, the excellent performance of the TVFP-ZNN model is verified through rigorous mathematical validation.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
