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Fixed-time synchronization of fuzzy stochastic delayed memristor-based neural networks subject to algebraic constraints 代数约束下模糊随机延迟忆阻器神经网络的定时同步
IF 3.9 2区 数学 Q1 MATHEMATICS, APPLIED Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109686
Xiang Wu, Hai Zhang, Xiaofeng Ye, Wenqi Zhang, Jinde Cao
This study investigates the fixed-time synchronization problem for stochastic delayed memristor-based neural networks with algebraic constraints within the T-S fuzzy logic framework. Algebraic constraints are first incorporated to accurately capture the inherent constrained properties of memristor-based neural networks. Then, the fixed-time stability lemma is extended to further shorten the synchronization convergence time. Leveraging this lemma, an appropriate Lyapunov function is constructed and a strictly aperiodic intermittent control strategy is developed, from which sufficient conditions and an explicit settling time for fixed-time synchronization are derived. The strictly aperiodic intermittent control mechanism effectively avoids unnecessary costs. Simulation results validate the theoretical analysis and demonstrate the enhanced performance of the proposed control scheme.
在T-S模糊逻辑框架下,研究了具有代数约束的随机延迟忆阻器神经网络的固定时间同步问题。首先引入代数约束来准确捕捉基于忆阻器的神经网络固有的约束特性。然后,对固定时间稳定性引理进行了推广,进一步缩短了同步收敛时间。利用这一引理,构造了合适的Lyapunov函数,提出了严格非周期间歇控制策略,并由此导出了固定时间同步的充分条件和显式稳定时间。严格的非周期间歇控制机制有效地避免了不必要的成本。仿真结果验证了理论分析的正确性,并证明了所提控制方案的性能有所提高。
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引用次数: 0
Bifurcation analysis of a two-infection transmission model with explicit vector dynamics. 具有显式媒介动力学的双感染传播模型的分岔分析。
IF 2.3 4区 数学 Q2 BIOLOGY Pub Date : 2026-01-10 DOI: 10.1007/s00285-026-02341-1
Akhil Kumar Srivastav, Vanessa Steindorf, Bruno V Guerrero, Nico Stollenwerk, Bob W Kooi, Maíra Aguiar

Dengue fever is a major public health problem and has been extensively modeled. Understanding the role of explicit vector dynamics in vector-borne diseases such as dengue fever is essential for accurately capturing transmission patterns and improving control strategies. In this study, we extend the minimalistic two-infection host-host SIRSIR model by introducing the SIRSIR-UV model, which explicitly incorporates vector population dynamics. Our aim is to investigate how these explicit vector dynamics influence the behavior of the system. In doing so, we extend previous models that assumed implicit vector effects in addition to immunity and disease enhancement factors. Using tools from nonlinear dynamics and bifurcation theory, we derive analytical conditions for transcritical and tangent bifurcations, formalize backward bifurcation using center manifold theory, and compute Hopf and global homoclinic bifurcation curves. We also show that seasonal influences in the vector populations, mimicking the seasonality of mosquitoes, contribute to the occurrence of chaotic behavior in disease transmission, reflecting the current patterns observed in epidemiological data. We thoroughly characterize the dynamics of the SIRSIR-UV model and explore the implications of including explicit vector dynamics. Finally, we discuss our results with the previous SIRSIR model and conclude that the bifurcation structures observed in the SIRSIR-UV model are consistent with those of the minimalistic SIRSIR model. This unexpected result has important implications for the modeling of vector-borne diseases. It suggests that simplifying assumptions, such as the use of implicit vector dynamics, can effectively capture important aspects of disease transmission while reducing the complexity of the mathematical analysis.

登革热是一个主要的公共卫生问题,已经广泛建立了模型。了解明确的病媒动力学在登革热等病媒传播疾病中的作用,对于准确捕捉传播模式和改进控制策略至关重要。在这项研究中,我们通过引入SIRSIR- uv模型扩展了简约的双感染宿主-宿主SIRSIR模型,该模型明确地包含了媒介种群动态。我们的目的是研究这些显式矢量动力学如何影响系统的行为。在这样做的过程中,我们扩展了先前的模型,这些模型假设除了免疫和疾病增强因素之外还有隐含的载体效应。利用非线性动力学和分岔理论的工具,导出了跨临界分岔和切线分岔的解析条件,利用中心流形理论形式化了后向分岔,并计算了Hopf曲线和全局同斜分岔曲线。我们还表明,媒介种群的季节性影响,模仿蚊子的季节性,有助于疾病传播中混乱行为的发生,反映了流行病学数据中观察到的当前模式。我们彻底表征了SIRSIR-UV模型的动力学,并探讨了包括显式矢量动力学的含义。最后,我们将我们的结果与先前的SIRSIR模型进行了讨论,并得出结论,在SIRSIR- uv模型中观察到的分岔结构与简约的SIRSIR模型一致。这一意想不到的结果对媒介传播疾病的建模具有重要意义。它表明,简化假设,例如使用隐式矢量动力学,可以有效地捕捉疾病传播的重要方面,同时降低数学分析的复杂性。
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引用次数: 0
Resilient Leader-following Consensus Control Approach for Generic Linear Multi-agent Systems under Input Saturation and DoS Cyberattacks 输入饱和和DoS网络攻击下的线性多智能体系统弹性领导者跟随共识控制方法
IF 3.9 2区 数学 Q1 MATHEMATICS, APPLIED Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109729
Sami Ullah, Fatima Tahir, Muhammad Rehan, Ijaz Ahmed, Muhammad Khalid
The paper investigates the resilient leader-following consensus of linear multi-agent systems (MASs) in the presence of input saturation and denial-of-service (DoS) cyberattacks. The present work ensures the leader-following consensus of MASs despite input saturation and DoS attacks, unlike conventional consensus control schemes that cannot address input saturation. In earlier works, large control inputs were applied to recover MASs from DoS attacks, which can lead to saturation of actuators. The saturation of actuators can assist attackers in carrying out their malicious activities. To address this problem, the saturation properties are incorporated in the design to consider a region of stability (RoS) under DoS attacks. The proposed approach uses buffers for improving the security of MASs, and it also estimates the allowable DoS frequency and DoS duration for a sustainable operation. Furthermore, the proposed work establish a region of initial conditions that guarantees the secure consensus under input saturation. The present scheme also reduce the computational complexity by converting the results into a solvable form using the properties of the Kronecker product. Finally, a numerical example is provided to verify the resultant scheme.
本文研究了线性多智能体系统(MASs)在输入饱和和拒绝服务(DoS)网络攻击下的弹性领导-跟随共识问题。与传统的共识控制方案不能解决输入饱和问题不同,目前的工作确保了在输入饱和和DoS攻击的情况下,MASs的leader-follow共识。在早期的工作中,大的控制输入被应用于从DoS攻击中恢复质量,这可能导致执行器饱和。执行器的饱和可以帮助攻击者进行恶意活动。为了解决这个问题,饱和特性被纳入到设计中,以考虑DoS攻击下的稳定区域(RoS)。提出的方法使用缓冲区来提高MASs的安全性,并且还估计了可持续操作的允许DoS频率和DoS持续时间。此外,提出的工作建立了一个初始条件区域,以保证在输入饱和下的安全共识。本方案还通过利用克罗内克积的性质将结果转换为可解形式来降低计算复杂度。最后给出了一个数值算例,对所得方案进行了验证。
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引用次数: 0
A Deep Implicit-Explicit Minimizing Movement Method for Partial Integro-Differential Equations, with application to Option Pricing in Jump-Diffusion Models 部分积分-微分方程的隐式-显式深度最小化运动方法及其在跳跃-扩散模型期权定价中的应用
IF 3.9 2区 数学 Q1 MATHEMATICS, APPLIED Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109709
Emmanuil H. Georgoulis, Antonis Papapantoleon, Costas Smaragdakis
We develop a novel deep learning approach for solving partial integro-differential equations (PIDEs) in high dimensions, involving diffusion and drift terms. To showcase its practicality and versatility, the methodology is presented for the specific challenge of pricing European basket options written on assets that follow jump-diffusion dynamics. The option pricing problem is formulated as a partial integro-differential equation, which is approximated via a new implicit-explicit minimizing movement time-stepping approach, involving approximation by deep, residual-type Artificial Neural Networks (ANNs) for each time step. The integral operator is discretized via two different approaches: (a) a sparse-grid Gauss–Hermite approximation following localised coordinate axes arising from singular value decompositions, and (b) an ANN-based high-dimensional special-purpose quadrature rule. Crucially, the proposed ANN is constructed to ensure the appropriate asymptotic behavior of the solution for large values of the underlyings and also leads to consistent outputs with respect to a priori known qualitative properties of the solution. The performance and robustness with respect to the dimension of these methods are assessed in a series of numerical experiments involving the Merton jump-diffusion model, while a comparison with the deep Galerkin method and the deep BSDE solver with jumps further supports the merits of the proposed approach.
我们开发了一种新的深度学习方法来解决涉及扩散和漂移项的高维偏积分微分方程(PIDEs)。为了展示其实用性和多功能性,本文提出了一种基于跳跃-扩散动力学的资产的欧洲一揽子期权定价的具体挑战。将期权定价问题表述为一个偏积分微分方程,该方程通过一种新的隐式-显式最小化运动时间步逼近方法进行逼近,该方法采用深度残差型人工神经网络(ann)对每个时间步进行逼近。积分算子通过两种不同的方法离散化:(a)基于奇异值分解产生的局部坐标轴的稀疏网格高斯-埃尔米特近似,以及(b)基于人工神经网络的高维专用正交规则。至关重要的是,所提出的人工神经网络的构造是为了确保对于大的基础值的解的适当渐近行为,并且还导致相对于先验已知的解的定性性质的一致输出。在Merton跳跃-扩散模型的一系列数值实验中,对这些方法的性能和鲁棒性进行了评估,同时与深度伽辽金方法和带跳跃的深度BSDE求解器的比较进一步支持了所提方法的优点。
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引用次数: 0
Unified Adaptive Fuzzy Control for Input Saturated Stochastic Nonlinear Systems With Multi-type Constraints 输入饱和多约束随机非线性系统的统一自适应模糊控制
IF 3.9 2区 数学 Q1 MATHEMATICS, APPLIED Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109692
Yan Yao, Jieqing Tan, Yangang Yao, Peng Chen
This paper proposes a unified adaptive fuzzy control approach for input saturated stochastic nonlinear systems (SNS) with multi-type constraints. Existing control methods typically require constraints to be continuous, which often differs from practical constraints. The “multiple types of constraints” studied in this article are mainly divided into four typical categories: full-state constraints (limiting the entire state space of the system), unconstrained situations (the basic situation without any constraint conditions), interval constraints (limiting the variables within a specified range), and performance constraints (ensuring that the system performance meets the preset requirements). By incorporating an auxiliary function, a unified mapping function is devised to address multi-type constraints without requiring the feasibility condition (FC). Moreover, to tackle the input saturation problem, a dual-power nonlinear feedback auxiliary subsystem is constructed, which employs a dual-regulation mechanism characterized by “strong feedback for small errors and fast decay for large errors” to overcome the convergence speed degradation problem in traditional approaches. And leveraging predefined-time stability theory and fuzzy logic systems (FLS), the proposed strategy can preset the stabilization time independent on initial states and control parameters, strictly adhere to constraints and effectively reduce the adverse effects of input saturation nonlinearity. Simulation results demonstrate the effectiveness and superiority of the proposed method.
针对输入饱和多约束随机非线性系统,提出了一种统一的自适应模糊控制方法。现有的控制方法通常要求约束是连续的,这通常与实际约束不同。本文研究的“多类型约束”主要分为四类典型:全状态约束(限制系统的整个状态空间)、无约束情况(没有任何约束条件的基本情况)、区间约束(将变量限制在指定范围内)和性能约束(保证系统性能满足预设要求)。通过合并辅助函数,设计了一个统一的映射函数,可以在不需要可行性条件(FC)的情况下处理多类型约束。此外,为了解决输入饱和问题,构建了双功率非线性反馈辅助子系统,采用“小误差强反馈、大误差快衰减”的双调节机制,克服了传统方法的收敛速度退化问题。利用预定义时间稳定性理论和模糊逻辑系统(FLS),该策略可以实现与初始状态和控制参数无关的预定义稳定时间,严格遵守约束条件,有效降低输入饱和非线性的不利影响。仿真结果验证了该方法的有效性和优越性。
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引用次数: 0
Graph-instructed neural networks for sparse grid-based discontinuity detectors 基于稀疏网格的不连续检测器的图指示神经网络
IF 4 2区 数学 Q1 MATHEMATICS, APPLIED Pub Date : 2026-01-10 DOI: 10.1016/j.amc.2025.129946
Francesco Della Santa, Sandra Pieraccini
In this paper, we present a novel approach for detecting the discontinuity interfaces of a discontinuous function. This approach leverages Graph-Instructed Neural Networks (GINNs) and sparse grids to address discontinuity detection even in domains of dimension larger than 3. GINNs, trained to identify troubled points on sparse grids, exploit graph structures built on the grids to achieve efficient and accurate discontinuity detection performance. We also introduce a recursive algorithm for general sparse grid-based detectors, characterized by convergence properties and ease of applicability. Numerical experiments on functions with dimensions n=2 and n=4 demonstrate the efficiency and robust generalization properties of GINNs in detecting discontinuity interfaces; test cases with n=6 and n=8 show the applicability of the method when the discontinuity interface presents specific structures. Notably, the trained GINNs offer portability and versatility, allowing integration into various algorithms and sharing among users.
本文提出了一种检测不连续函数的不连续界面的新方法。这种方法利用图指示神经网络(ginn)和稀疏网格来解决即使在维度大于3的域中也能进行不连续检测。ginn通过训练来识别稀疏网格上的问题点,利用建立在网格上的图结构来实现高效准确的不连续检测性能。我们还介绍了一种用于一般稀疏网格检测器的递归算法,该算法具有收敛性和易于应用的特点。在n=2和n=4维函数上的数值实验证明了ginn在检测不连续界面方面的有效性和鲁棒性;n=6和n=8的测试用例显示了该方法在不连续界面呈现特定结构时的适用性。值得注意的是,经过训练的ginn提供了可移植性和多功能性,允许集成到各种算法中并在用户之间共享。
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引用次数: 0
Reinforcement learning-based optimal false data injection attack against control signals in probabilistic Boolean control networks 基于强化学习的概率布尔控制网络控制信号最优假数据注入攻击
IF 3.4 2区 数学 Q1 MATHEMATICS, APPLIED Pub Date : 2026-01-10 DOI: 10.1016/j.amc.2025.129939
Xianlun Peng , Fangfei Li , Yang Liu
In this paper, we focus on the challenges of optimal false-data injection attacks in Probabilistic Boolean Control Networks (PBCNs) where attackers lack system model knowledge. Attack optimization is formulated as a Markov Decision Process (MDP) policy search problem. Theoretical guidance for reward design in the MDP is provided, proving the existence of optimal policies under Theorem 1. An enhanced Q-learning (QL) algorithm is developed to overcome scalability limitations of the standard QL in large-scale PBCNs. Experimental validation on both 10-node and 28-node networks demonstrates the efficacy of the method.
本文重点研究了概率布尔控制网络(PBCNs)中攻击者缺乏系统模型知识的最优假数据注入攻击所面临的挑战。攻击优化是一个马尔可夫决策过程(MDP)策略搜索问题。为MDP下的奖励设计提供了理论指导,证明了定理1下最优策略的存在性。为了克服标准QL在大规模PBCNs中的可扩展性限制,提出了一种增强型Q-learning (QL)算法。在10节点和28节点网络上的实验验证表明了该方法的有效性。
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引用次数: 0
Flux Quantization in Type II Superconductors II型超导体的通量量子化
IF 1.2 2区 数学 Q2 MATHEMATICS, APPLIED Pub Date : 2026-01-10 DOI: 10.1007/s00006-025-01409-3
Gene E. McClellan

This paper explores the physics of magnetic and electric flux tubes supported by current vortices in condensed matter having a superconducting state in which bosonic charge carriers flow without resistance. The starting point is that the boson wave function satisfies the Klein–Gordon equation of relativistic quantum mechanics. Next, the electromagnetic fields within the superconducting medium are assumed to obey the quasistatic Maxwell equations expressed with geometric algebra and calculus and incorporating either electric or hypothetical magnetic currents. Finally, the Fundamental Theorem of Calculus is utilized in two forms to examine flux tubes, first in electric superconductors and then in hypothetical magnetic superconductors. Geometric algebra and calculus enable a consistent treatment of both analyses and an extension from three to four spatial dimensions.

本文探讨了在超导状态下玻色子载流子无阻力流动的凝聚态物质中,由电流涡流支撑的磁通管和电通管的物理性质。出发点是玻色子波函数满足相对论量子力学的Klein-Gordon方程。其次,假定超导介质中的电磁场服从用几何代数和微积分表示的准静态麦克斯韦方程,并包含电流或假设的磁流。最后,微积分基本定理以两种形式被用来检验磁通管,首先是在电超导体中,然后是在假设的磁超导体中。几何代数和微积分使分析和扩展从三到四个空间维度的一致处理成为可能。
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引用次数: 0
On Lie Groups Preserving Subspaces of Degenerate Clifford Algebras 退化Clifford代数的李群保持子空间
IF 1.2 2区 数学 Q2 MATHEMATICS, APPLIED Pub Date : 2026-01-10 DOI: 10.1007/s00006-025-01431-5
Ekaterina Filimoshina, Dmitry Shirokov

This paper introduces Lie groups in degenerate geometric (Clifford) algebras that preserve four fundamental subspaces determined by the grade involution and reversion under the adjoint and twisted adjoint representations. We prove that these Lie groups can be equivalently defined using norm functions of multivectors applied in the theory of spin groups. We also study the corresponding Lie algebras. Some of these Lie groups and algebras are closely related to Heisenberg Lie groups and algebras. The introduced groups are interesting for various applications in physics and computer science, in particular, for constructing equivariant neural networks.

本文介绍了退化几何代数中的李群,它们在伴伴表示和扭曲伴伴表示下保留了四个基本子空间,这些子空间是由级数对合和反转决定的。利用自旋群理论中的多向量范数函数证明了这些李群可以等价地定义。我们还研究了相应的李代数。其中一些李群和代数与海森堡李群和代数密切相关。引入的群对于物理和计算机科学中的各种应用非常有趣,特别是对于构造等变神经网络。
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引用次数: 0
Observer-based adaptive secure consensus for nonlinear multi-agent systems: A dynamic event-triggered strategy 非线性多智能体系统中基于观测器的自适应安全共识:一种动态事件触发策略
IF 3.9 2区 数学 Q1 MATHEMATICS, APPLIED Pub Date : 2026-01-10 DOI: 10.1016/j.cnsns.2026.109724
Yongjie Tian, Huiyan Zhang, Yongchao Liu, Ning Zhao, Imre J. Rudas
This article addresses a distributed dynamic event-triggered adaptive fuzzy output feedback consensus problem for nonlinear multi-agent systems (MASs) under deception attacks (DAs). The primary challenge confronted is unknown DAs on the sensor-controller communication channel and output states on feedback design unavailable simultaneously. To solve this obstacle, an improved adaptive fuzzy observer based on the post-triggered compromised output information (PTCOI) is designed to estimate the unavailable states under the backstepping design approach. For the case of virtual control non-differentiable due to the non-smoothness of the PTCOI, a novel coordinate transformation with estimated states is constructed. Moreover, to relieve the communication burden of the MASs, a dynamic event-triggered mechanism with the tradeoff resource constraints on communication channel and consensus tracking performance is developed. The proposed controller can not only make all followers maintain consensus with the trajectory of the leader but also guarantee all signals remain bounded. Finally, the availability of the developed methodology is demonstrated with a robotic system with four links as an example.
本文研究了欺骗攻击下非线性多智能体系统(MASs)的分布式动态事件触发自适应模糊输出反馈一致性问题。所面临的主要挑战是传感器控制器通信信道上的未知DAs和反馈设计上的输出状态同时不可用。为了解决这一问题,设计了一种基于后触发折衷输出信息(PTCOI)的改进自适应模糊观测器来估计退步设计方法下的不可用状态。针对PTCOI的非光滑性导致虚拟控制不可微的情况,构造了一种带估计状态的坐标变换。此外,为了减轻MASs的通信负担,开发了一种动态事件触发机制,该机制在通信通道和共识跟踪性能上权衡了资源约束。所提出的控制器既能使所有follower与leader的轨迹保持一致,又能保证所有信号保持有界。最后,以具有四个连杆的机器人系统为例,验证了所开发方法的有效性。
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引用次数: 0
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