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基于离散忆阻器的复值混沌系统动力学分析及其在双图像加密中的应用

邓全利 王春华 杨港

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基于离散忆阻器的复值混沌系统动力学分析及其在双图像加密中的应用

邓全利, 王春华, 杨港
cstr: 32037.14.aps.75.20251242

Discrete memristor-based complex-valued chaotic system dynamic analysis and its application in dual-image encryption

DENG Quanli, WANG Chunhua, YANG Gang
cstr: 32037.14.aps.75.20251242
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  • 设计新型混沌系统能够丰富加密系统的候选资源, 是基于混沌加密安全性的重要途径. 离散忆阻器因其固有的非线性特性与电路友好特性, 为构建新型混沌系统提供了有效途径. 然而, 其在复值离散混沌系统中的应用仍有待探索. 为此, 本文构建了一种基于离散忆阻器的复高斯混沌模型, 其中忆阻器由复数模长驱动. 通过李雅普诺夫指数、分岔图和相图等数值仿真分析, 验证了该系统具有增强的混沌特性. 同时, 在FPGA数字平台上实现了该模型的硬件部署, 证明其硬件可行性. 基于该模型生成的复值混沌序列, 本文进一步设计了一种双图像加密方案, 将两幅图像视为复数矩阵的实部和虚部, 通过混沌序列进行置乱和扩散操作. 仿真结果表明, 该加密方案具有高安全性, 能够抵抗多种攻击.
    The exploration of complex-valued chaos not only provides a feasible approach for practical applications such as image encryption, but also has great potential in simulating wave phenomena and quantum inspired process. In order to bridge it with nonlinear circuit components, we introduce a novel complex-valued chaotic system by embedding a discrete memristor into a complex Gaussian map. The memristor, a component with inherent physical memory, is uniquely driven by the modulus of the complex state variable, which is a key physical quantity often related to energy or amplitude in wave systems. This coupling induces complex nonlinear dynamics, which are physically characterized through Lyapunov exponents and bifurcation analysis, revealing an enhanced and more robust chaotic regime. The physical feasibility of this system is demonstrated by its successful hardware realization on an FPGA platform. To demonstrate its potential applications, we leverage the complex chaotic flows of the system to engineer a dual-image encryption scheme, where the encryption process is explained as a physical diffusion and scrambling of information represented by a complex matrix. Our results verify that this approach not only yields a cryptosystem with high security but also provides a link between complex chaos and information security applications.
      通信作者: 王春华, wch1227164@hnu.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 62571183, 62271197)和广东省基础与应用基础研究基金(批准号: 2024A1515011910)资助的课题.
      Corresponding author: WANG Chunhua, wch1227164@hnu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 62571183, 62271197) and the Basic and Applied Basic Research Foundation of Guangdong Province, China (Grant No. 2024A1515011910).
    [1]

    周双, 尹彦力, 王诗雨, 张盈谦 2024 73 210501Google Scholar

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    [2]

    Bao H, Wang R M, Tang H G, Chen M, Bao B C 2025 IEEE Internet Things J. 12 20902Google Scholar

    [3]

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    [4]

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    Lai Q, Wang J, Huang D X 2025 Acta Phys. Sin. 74 200501Google Scholar

    [5]

    Deng Q L, Wang C H, Yang G, Luo D W 2025 IEEE Internet Things J. 12 25559Google Scholar

    [6]

    Yu F, He S Q, Yao W, Cai S, Xu Q 2025 IEEE Trans. Comput. Aid. Des. 44 1Google Scholar

    [7]

    Zhou L L, Lin Z Q, Tan F, Chen P Y 2025 Expert Syst. Appl. 281 127475Google Scholar

    [8]

    Luo D W, Wang C H, Liang J H, Deng Q L 2025 Nonlinear Dyn. 113 29983Google Scholar

    [9]

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    [10]

    Li H D, Min F H 2025 IEEE Int. Things J. 12 29018Google Scholar

    [11]

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    [12]

    王璇, 杜健嵘, 李志军, 马铭磷, 李春来 2024 73 110503Google Scholar

    Wang X, Du J R, Li Z J, Ma M L, Li C L 2024 Acta Phys. Sin. 73 110503Google Scholar

    [13]

    Yu F, Tan B H, He T, He S Q, Huang Y Y, Cai S, Lin H R 2025 Mathematics 13 726Google Scholar

    [14]

    Zhang Y X, Hua Z Y, Bao H, Huang H J 2024 IEEE Trans. Circuits Syst. I 71 2783Google Scholar

    [15]

    Hua Z Y, Yao J H, Zhang Y X, Bao H, Yi S 2025 IEEE Trans. Ind. Inf. 21 85Google Scholar

    [16]

    Yao J H, Zhang Y X, Bao H, Hua Z Y 2025 Chaos Soliton. Fract. 197 116453Google Scholar

    [17]

    Rani M, Agarwal R 2009 Chaos Soliton. Fract. 42 447Google Scholar

    [18]

    Ayubi P, Barani M J, Valandar M Y, Irani B Y, Sadigh R S M 2021 Artif. Intell. Rev. 54 1237Google Scholar

    [19]

    Yu Y J, Ren S G, Yang L, Li Y, Miao X S 2025 Sci. China Inf. Sci. 68 139402Google Scholar

    [20]

    Deng Q L, Wang C H, Sun Y C, Xu C, Lin H R, Deng Z K 2025 IEEE Trans. Comput. Aid. Des. 44 4701Google Scholar

    [21]

    Zhang X, Li C B, Moroz I, Huang K, Liu Z H 2025 Nonlinear Dyn. 113 15487Google Scholar

    [22]

    Bao H, Fan J H, Hua Z Y, Xu Q, Bao B C 2025 IEEE Internet Things J. 12 31843Google Scholar

    [23]

    Gao S, Ho-Ching-Iu H, Erkan U, Simsek C, Toktas A, Cao Y H, Wu R, Mou J, Li Q, Wang C P 2025 IEEE Trans. Circuits Syst. Video Technol. 35 7706Google Scholar

    [24]

    Al Qurashi M, Asif Q U A, Chu Y M, Rashid S, Elagan S K 2023 Results Phys. 51 106627Google Scholar

    [25]

    Almatroud A O, Grassi G, Khennaoui A A, Abbes A, Ouannas A, Alshammari S, Albosaily S 2024 Alexandria Eng. J. 93 1Google Scholar

    [26]

    Li H D, Min F H 2025 IEEE Trans. Circuits Syst. I 72 4820Google Scholar

    [27]

    Wang C H, Li Y F, Yang G, Deng Q L 2025 Mathematics 13 1600Google Scholar

    [28]

    Yu F, Zhang S K, Su D, Wu Y Y, Yumba Musoya G, Yin H G 2025 Fractal Fractional 9 115Google Scholar

    [29]

    Deng Q L, Wang C H, Sun Y C, Yang G 2025 IEEE Trans. Circuits Syst. I 72 300Google Scholar

    [30]

    Luo D W, Wang C H, Deng Q L, Yang G 2025 Nonlinear Dyn. 113 28381Google Scholar

    [31]

    Fan C L, Ding Q 2025 Chaos Soliton. Fract. 191 115905Google Scholar

    [32]

    Zhang S, He D Z, Li Y X, Lu D R, Li C B 2025 IEEE Trans. Autom. Sci. Eng. 22 17828Google Scholar

    [33]

    Yu F, Su D, He S Q, Wu Y Y, Zhang S K, Yin H G 2025 Chin. Phys. B 34 050502Google Scholar

    [34]

    张红伟, 付常磊, 潘志鹏, 丁大为, 王金, 杨宗立, 刘涛 2024 73 180501Google Scholar

    Zhang H W, Fu C L, Pan Z P, Ding D W, Wang J, Yang Z L, Liu T 2024 Acta Phys. Sin. 73 180501Google Scholar

    [35]

    赖强, 秦铭宏 2025 电子信息学报 47 3262Google Scholar

    Lai Q, Qin M H 2025 J. Electron. Inf. Technol. 47 3262Google Scholar

    [36]

    赖强, 秦铭宏 2025 贵州师范大学学报(自然科学版) 43 1Google Scholar

    Lai Q, Qin M H 2025 J. Guizhou Norm. Univ. (Nat. Sci.) 43 1Google Scholar

    [37]

    赖强, 王君 2024 73 180503Google Scholar

    Lai Q, Wang J 2024 Acta Phys. Sin. 73 180503Google Scholar

    [38]

    Yu F, Yumba Musoya G, Guo R, Ying Z, Xu J, Yao W, Jin J, Lin H 2025 Axioms 14 638Google Scholar

    [39]

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    [40]

    An X L, Liu S Y, Li X, Zhang J G, Li X Y 2024 Expert Syst. Appl. 243 122899Google Scholar

    [41]

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    [42]

    Zhong Y M, Lai Q, Zhu C K, Qin M H 2026 Comput. Stand. Interfaces 95 104051Google Scholar

    [43]

    Zhao Y, Zheng M, Zhang Y, Yuan M, Zhou H 2024 Nonlinear Dyn. 112 19515Google Scholar

  • 图 1  施加$ v_n=A\sin (\omega n) $时忆阻器的磁滞回线 (a)固定A = 0.03, ω分别取11.25, 18, 37.5; (b) 固定ω = 11.25, A分别取0.03, 0.05, 0.1

    Fig. 1.  Pinched hysteresis loops of the memristor when applying $ v_n=A\sin (\omega n) $: (a) Fixed A = 0.03, ω selected as 11.25, 18 and 37.5; (b) fixed ω = 11.25, A selected as 0.03, 0.05 and 0.1.

    图 2  忆阻器复值高斯混沌系统的构成框图

    Fig. 2.  Structure diagram of the memristive complex-valued Gaussian map

    图 3  依赖于实数参数的动力学行为仿真 (a) 前两个李雅普诺夫指数; (b) $ z_{\mathrm{r}} $(绿色)与$ z_{\mathrm{i}} $(粉色)的分岔图

    Fig. 3.  The real-valued parameter a-dependent dynamical behavior simulations: (a) First two Lyapunov exponents; (b) bifurcation of $ z_{\mathrm{r}} $ (green colored) and $ z_{\mathrm{i}}$ (pink colored)

    图 4  依赖于耦合参数b的动力学行为仿真 (a1), (a2) 固定$ b_{\mathrm{i}} = 0.1 $, $ b_{\mathrm{r}}\in[0, 1] $; (b1), (b2)固定$ b_{\mathrm{r}} = 0.1 $,$ b_{\mathrm{i}} \in [-0.8, 0.8]$

    Fig. 4.  The coupling parameter b-dependent dynamical behavior simulations: (a1), (a2) $ b_{\mathrm{r}}\in[0, 1] $ with $ b_{\mathrm{i}}=0.1 $; (b1), (b2) $ b_{\mathrm{i}}\in[-0.8, 0.8] $ with $ b_{\mathrm{r}}=0.1 $

    图 5  在$ y{\text{-}}z_{\mathrm{r}} $(红色)和$ y{\text{-}}z_{\mathrm{i}} $(蓝色)平面上的相图 (a) 混沌吸引子, $ b_{\mathrm{r}} $ = 0.1; (b) 周期吸引子, $ b_{\mathrm{r}} $ = 0.3; (c) 混沌吸引子, $ b_{\mathrm{r}} $ = 0.4; (d) 准周期吸引子, $ b_{\mathrm{r}} $ = 0.9

    Fig. 5.  Phase portrait in $ y \text{-}z_{\mathrm{r}} $ (red) and $ y \text{-}z_{\mathrm{i}} $ (blue) plane: (a) Chaotic attractor with $ b_{\mathrm{r}} $ = 0.1; (b) periodic attractor with $ b_{\mathrm{r}} $ = 0.3; (c) chaotic attractor with $ b_{\mathrm{r}} $ = 0.4; (d) quasi-periodic attractor with $ b_{\mathrm{r}} $ = 0.9

    图 6  状态变量的分岔图 (a) 原始系统; (b) 施加变量旋转后; (c) 施加参数复化后; (d) 施加复共轭后

    Fig. 6.  Bifurcation diagram of $ z_{\mathrm{r }}$: (a) Original system; (b) under variable rotation; (c) under parameter complexification; (d) under complex conjugation.

    图 7  李雅普诺夫指数 (a) 原始系统; (b) 施加变量旋转后; (c) 施加参数复化后; (d) 施加复共轭后

    Fig. 7.  Lyapunov exponents: (a) Original system; (b) under variable rotation; (c) under parameter complexification; (d) under complex conjugation.

    图 8  当$ b_{\mathrm{r}}=1 $时的相图 (a) 原始系统; (b) 施加变量旋转后; (c) 施加参数复化后; (d) 施加复共轭后

    Fig. 8.  Phase portraits with $ b_{\mathrm{r}}= 1 $: (a) Original system; (b) under variable rotation; (c) under parameter complexification; (d) under complex conjugation.

    图 9  基于FPGA实现MCVGM的结构框图

    Fig. 9.  Block diagram for the FPGA-based implementation of MCVGM

    图 10  基于FPGA硬件实验产生的混沌吸引子 (a) $ b_{\mathrm{r}} $ = 0.1; (b) $ b_{\mathrm{r}} $ = 0.4

    Fig. 10.  FPGA-based experimental chaotic attractor: (a) $ b_{\mathrm{r}} $=0.1; (b) $ b_{\mathrm{r}} $=0.4

    图 11  基于FPGA产生混沌序列与数值仿真之间的误差统计 (a)绝对误差分布; (b)绝对误差累积分布函数; (c)误差指标对比; (d)绝对误差分布直方图

    Fig. 11.  Error statistics between FPGA-generated chaotic sequence and numerical simulation: (a) Absolute error distribution; (b) cumulative distribution function of absolute errors; (c) comparison of error metrics; (d) histogram of absolute error distribution.

    图 12  双图像复值加密系统流程图

    Fig. 12.  Flowchart of the dual-image complex-valued encryption system

    图 13  像素直方图测试 (a1)—(d1) 原始图像; (a2)—(d2) 原始图像的像素直方图; (a3)—(d3) 加密图像的像素直方图

    Fig. 13.  Histogram test of cryptosystem: (a1)—(d1) Original image; (a2)—(d2) histogram of original image; (a3)—(d3) histogram of encrypted image.

    图 14  解密结果 (a1), (b1)使用正确密钥得到的还原图像; (a2), (b2)初始条件$ z_0 $仅有微小偏差时的解密图像

    Fig. 14.  Decrypted images: (a1), (b1) With correct secret keys; (a2), (b2) with tiny variation in the initial condition $ z_0 $

    图 15  图像的像素相关性分布 (a1)水平方向, (a2)垂直方向, (a3)对角方向的原始图像; (b1)水平方向, (b2)垂直方向, (b3)对角方向的加密图像

    Fig. 15.  Distribution of pixel correlations in images: Original image in (a1) horizontal direction, (a2) vertical direction, (a3) diagonal direction; encrypted image in (b1) horizontal direction, (b2) vertical direction, (b3) diagonal direction.

    图 16  数据丢失与噪声攻击测试结果 (a1) 图像I1密文遭受30%数据丢失后的图像; (a2) 从数据丢失后的密文恢复出的图像; (b1) 图像I1密文添加强度为30%的椒盐噪声后的图像; (b2) 从被添加噪声后的密文中恢复的图像

    Fig. 16.  Test results of data loss and noise attacks: (a1) Ciphertext images of I1 with 30% loss; (a2) recovered images from ciphertext after data loss; (b1) ciphertext images of I1 with 30% intensity of pepper and salt noise; (b2) recovered images from the ciphertext after adding noise.

    表 1  原始图像和加密图像的相关性系数

    Table 1.  Correlation coefficients of original and encrypted images

    图像 原始图像 密文图像
    H V D H V D
    I1 Cameraman 0.9576 0.9259 0.9147 0.0015 0.0027 –0.0034
    House 0.9669 0.9460 0.9779 0.0012 –0.0021 0.0011
    I2 Pepper 0.9439 0.9585 0.9106 –0.0092 0.0071 –0.0099
    Starfish 0.9630 0.9357 0.8983 –0.0016 0.0037 –0.0055
    I3 Butterfly 0.9310 0.9388 0.9017 0.0080 –0.0011 0.0008
    Aircraft 0.9037 0.9122 0.8427 0.0023 –0.0088 0.0014
    I4 Parrot 0.9406 0.9532 0.9199 0.0037 0.0093 –0.0074
    Lena 0.9716 0.9437 0.9240 0.0019 –0.0010 0.0092
    下载: 导出CSV

    表 2  原始图像和加密图像的信息熵

    Table 2.  Information entropy of original and encrypted images

    图像 原始图像 密文图像
    I1 Cameraman 7.0875 7.9987
    House 6.7152 7.9989
    I2 Pepper 7.5498 7.9983
    Starfish 7.6959 7.9979
    I3 Butterfly 7.6420 7.9991
    Aircraft 6.9593 7.9989
    I4 Parrot 7.6624 7.9986
    Lena 7.6591 7.9979
    下载: 导出CSV

    表 3  与现有加密方案安全性能对比

    Table 3.  Comparison of security performance with existing encryption schemes

    安全指标 文献[42] 文献[43] 文献[41] 本文
    密钥空间 $ 2^{256} $ $ 2^{260} $ $ 2^{627} $ $ 2^{318} $
    密钥敏感性 $ 10^{-13} $ $ 10^{-14} $ $ 10^{-16} $
    信息熵 7.9944 7.9969 7.9915 7.9979
    相关性系数 0.0011 0.0043 0.0118 0.0040
    下载: 导出CSV
    Baidu
  • [1]

    周双, 尹彦力, 王诗雨, 张盈谦 2024 73 210501Google Scholar

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    [2]

    Bao H, Wang R M, Tang H G, Chen M, Bao B C 2025 IEEE Internet Things J. 12 20902Google Scholar

    [3]

    Deng Q L, Wang C H, Sun Y C, Yang G 2025 IEEE Trans. Cybern. 55 3926Google Scholar

    [4]

    赖强, 王君, 黄大勋 2025 74 200501Google Scholar

    Lai Q, Wang J, Huang D X 2025 Acta Phys. Sin. 74 200501Google Scholar

    [5]

    Deng Q L, Wang C H, Yang G, Luo D W 2025 IEEE Internet Things J. 12 25559Google Scholar

    [6]

    Yu F, He S Q, Yao W, Cai S, Xu Q 2025 IEEE Trans. Comput. Aid. Des. 44 1Google Scholar

    [7]

    Zhou L L, Lin Z Q, Tan F, Chen P Y 2025 Expert Syst. Appl. 281 127475Google Scholar

    [8]

    Luo D W, Wang C H, Liang J H, Deng Q L 2025 Nonlinear Dyn. 113 29983Google Scholar

    [9]

    Chen W, Wang Y C, Shi C, Shen G L, Li M Y, Liu Y, Hei X H 2025 Neural Netw. 191 107799Google Scholar

    [10]

    Li H D, Min F H 2025 IEEE Int. Things J. 12 29018Google Scholar

    [11]

    Bao H, Fan Z, Hua Z Y, Zhang Y Z, Xu Q, Bao B C 2025 Expert Syst. Appl. 290 128448Google Scholar

    [12]

    王璇, 杜健嵘, 李志军, 马铭磷, 李春来 2024 73 110503Google Scholar

    Wang X, Du J R, Li Z J, Ma M L, Li C L 2024 Acta Phys. Sin. 73 110503Google Scholar

    [13]

    Yu F, Tan B H, He T, He S Q, Huang Y Y, Cai S, Lin H R 2025 Mathematics 13 726Google Scholar

    [14]

    Zhang Y X, Hua Z Y, Bao H, Huang H J 2024 IEEE Trans. Circuits Syst. I 71 2783Google Scholar

    [15]

    Hua Z Y, Yao J H, Zhang Y X, Bao H, Yi S 2025 IEEE Trans. Ind. Inf. 21 85Google Scholar

    [16]

    Yao J H, Zhang Y X, Bao H, Hua Z Y 2025 Chaos Soliton. Fract. 197 116453Google Scholar

    [17]

    Rani M, Agarwal R 2009 Chaos Soliton. Fract. 42 447Google Scholar

    [18]

    Ayubi P, Barani M J, Valandar M Y, Irani B Y, Sadigh R S M 2021 Artif. Intell. Rev. 54 1237Google Scholar

    [19]

    Yu Y J, Ren S G, Yang L, Li Y, Miao X S 2025 Sci. China Inf. Sci. 68 139402Google Scholar

    [20]

    Deng Q L, Wang C H, Sun Y C, Xu C, Lin H R, Deng Z K 2025 IEEE Trans. Comput. Aid. Des. 44 4701Google Scholar

    [21]

    Zhang X, Li C B, Moroz I, Huang K, Liu Z H 2025 Nonlinear Dyn. 113 15487Google Scholar

    [22]

    Bao H, Fan J H, Hua Z Y, Xu Q, Bao B C 2025 IEEE Internet Things J. 12 31843Google Scholar

    [23]

    Gao S, Ho-Ching-Iu H, Erkan U, Simsek C, Toktas A, Cao Y H, Wu R, Mou J, Li Q, Wang C P 2025 IEEE Trans. Circuits Syst. Video Technol. 35 7706Google Scholar

    [24]

    Al Qurashi M, Asif Q U A, Chu Y M, Rashid S, Elagan S K 2023 Results Phys. 51 106627Google Scholar

    [25]

    Almatroud A O, Grassi G, Khennaoui A A, Abbes A, Ouannas A, Alshammari S, Albosaily S 2024 Alexandria Eng. J. 93 1Google Scholar

    [26]

    Li H D, Min F H 2025 IEEE Trans. Circuits Syst. I 72 4820Google Scholar

    [27]

    Wang C H, Li Y F, Yang G, Deng Q L 2025 Mathematics 13 1600Google Scholar

    [28]

    Yu F, Zhang S K, Su D, Wu Y Y, Yumba Musoya G, Yin H G 2025 Fractal Fractional 9 115Google Scholar

    [29]

    Deng Q L, Wang C H, Sun Y C, Yang G 2025 IEEE Trans. Circuits Syst. I 72 300Google Scholar

    [30]

    Luo D W, Wang C H, Deng Q L, Yang G 2025 Nonlinear Dyn. 113 28381Google Scholar

    [31]

    Fan C L, Ding Q 2025 Chaos Soliton. Fract. 191 115905Google Scholar

    [32]

    Zhang S, He D Z, Li Y X, Lu D R, Li C B 2025 IEEE Trans. Autom. Sci. Eng. 22 17828Google Scholar

    [33]

    Yu F, Su D, He S Q, Wu Y Y, Zhang S K, Yin H G 2025 Chin. Phys. B 34 050502Google Scholar

    [34]

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    Zhang H W, Fu C L, Pan Z P, Ding D W, Wang J, Yang Z L, Liu T 2024 Acta Phys. Sin. 73 180501Google Scholar

    [35]

    赖强, 秦铭宏 2025 电子信息学报 47 3262Google Scholar

    Lai Q, Qin M H 2025 J. Electron. Inf. Technol. 47 3262Google Scholar

    [36]

    赖强, 秦铭宏 2025 贵州师范大学学报(自然科学版) 43 1Google Scholar

    Lai Q, Qin M H 2025 J. Guizhou Norm. Univ. (Nat. Sci.) 43 1Google Scholar

    [37]

    赖强, 王君 2024 73 180503Google Scholar

    Lai Q, Wang J 2024 Acta Phys. Sin. 73 180503Google Scholar

    [38]

    Yu F, Yumba Musoya G, Guo R, Ying Z, Xu J, Yao W, Jin J, Lin H 2025 Axioms 14 638Google Scholar

    [39]

    Zhao Q H, Bao H, Zhang X, Wu H G, Bao B C 2024 Chaos Soliton. Fract. 182 114769Google Scholar

    [40]

    An X L, Liu S Y, Li X, Zhang J G, Li X Y 2024 Expert Syst. Appl. 243 122899Google Scholar

    [41]

    An T, Gao T, Chen T, Jiang D 2025 Complex Intell. Syst. 11 319Google Scholar

    [42]

    Zhong Y M, Lai Q, Zhu C K, Qin M H 2026 Comput. Stand. Interfaces 95 104051Google Scholar

    [43]

    Zhao Y, Zheng M, Zhang Y, Yuan M, Zhou H 2024 Nonlinear Dyn. 112 19515Google Scholar

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出版历程
  • 收稿日期:  2025-09-10
  • 修回日期:  2025-10-13
  • 上网日期:  2025-10-22

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