多维系统的约化常被转化为多元多项式矩阵与其Smith型的等价来考虑.文章主要研究一类多元多项式矩阵与其Smith型的幺模等价问题,首先通过证明给出了这类矩阵与其Smith型幺模等价的判别条件.其次将结果推广到非方阵和非满秩的情形.最后,给出了这类矩阵约化到其Smith型的一般算法,并通过一个实例进行验证说明.

文章主要研究边界带有匹配干扰的不确定变系数热方程的输出调节问题.该热系统由具有混合边界条件的不确定抛物型偏微分方程(PDE)描述,其空间扩散系数存在未知变化,且受到幅值可能无界的光滑不确定边界扰动影响.所提出的鲁棒控制策略融合了线性反馈设计与“Twisting”二阶滑模控制算法,并在无穷维空间中进行适当重构与结合.基于极大单调算子理论,证明了闭环系统(作为一类微分包含问题)的适定性.紧接着,通过引入非标准Lyapunov泛函,且假设控制器中的调节参数在满足一些基本不等式的条件下,证明了闭环系统在Sobolev空间中的全局渐近稳定性.文章中的数值模拟部分通过仿真实验验证了所提控制器设计的合理性.

基础教育资源的合理配置是实现教育公平与质量提升的关键,人力资本提升则是教育强国建设的核心驱动力.文章基于2000年至2022年的省级年度面板数据,运用固定效应模型和双重差分模型,验证基础教育资源可获性、优质性与均衡性对人力资本积累的影响.研究结果表明,基础教育资源的可获性对人力资本存量具有长期的正向影响,以初中阶段作用最为显著;基础教育资源的优质性对人力资本质量的影响存在阶段性差异.机制分析表明,教育资源的优质性通过吸引更多的高水平人口流入,进一步提升了地区的人力资本质量.基础教育资源的均衡性对人力资本结构的改善起到了积极作用,尤其是初中阶段.此外,通过双重差分模型进一步验证了教育均衡化发展政策对人力资本积累的积极影响.

梯度法是最简单也是最基本的求解最优化问题的计算方法之一.由于所有的梯度法都是沿着最速下降方向找下一个迭代点,不同的梯度法就在于步长的不同选取.BB步长是梯度方法的最著名的步长选取方式之一.本文通过把BB步看成是基于一维子空间逼近的步长,构造了基于二维子空间逼近、三维子空间逼近的新步长.新步长具有良好的理论性质,有望发展成为有效的数值方法.

文章主要讨论一类退化热-梁耦合偏微分动力学系统解的长时间渐近性态. 该模型由两部分组成:一部分由退化的热方程描述, 另一部分由Timoshenko梁方程描述. 两者在公共边界处通过一定的传输条件耦合连接在一起. 基于频域分析方法, 文章给出在光滑初始条件下该类耦合系统多项式稳定的精细估计, 确定了系统的衰减速率与热退化阶数的显式关系.

文章深入探讨了国有机构投资者持股对企业投资效率的影响机制, 特别是在混合所有制改革背景下, 结合理论分析与实证检验, 揭示了国有机构投资者在提升企业投资效率中的关键作用. 研究表明, 国有机构投资者持股比例的增加对提升企业投资效率具有显著促进作用, 此外, 公司治理水平、企业规模、行业竞争程度以及现金流波动性等因素也对企业投资效率产生直接影响. 进一步研究发现, 企业数字化转型和绿色创新在国有机构投资者持股与企业投资效率之间发挥了重要的中介作用, 国有机构投资者通过提供资金支持、战略指导和资源配置, 推动企业加速数字化转型和绿色创新进程, 并且数字化转型能通过提升信息处理能力、优化资源配置效率, 绿色创新则可通过提高资源利用效率、增强市场竞争力, 使得国有机构投资者持股亦能间接提升企业的投资效率. 文章不仅丰富了国有机构投资者持股经济后果的相关研究, 还为理解企业投资效率的提升机制提供了新的理论视角, 同时为新时代深化国有企业改革、优化国有资本布局以及提升企业整体竞争力提供了重要的理论依据和政策启示.

共享出行服务的快速发展为城市出行提供了新的解决方案. 网约车平台通过智能手机应用程序提供便捷的叫车服务, 能够更好地将用户需求与即时运力相匹配. 在拼车出行服务中, 乘客可以合乘同一辆车, 将各自的目的地作为途径点, 以提高车辆的利用率. 文章提出了一种基于自注意力强化学习网络的网约车订单合乘及派单优化模型. 首先, 构建了网约车合乘出行拓扑网络, 考虑乘客的上下车时间、地点等因素, 设计了按需合乘动态匹配算法, 识别网约车潜在合乘订单. 然后, 构建了基于自注意力强化学习网络优化网约车派单, 采用策略梯度算法进行学习和训练, 在派单过程中引入掩码方法保证派单决策的有效性, 依托其“离线训练+在线决策”的优势, 实现大规模网约车派单问题的高效求解. 最后, 基于北京市网约车订单数据进行了实际案例研究, 结果表明, 基于自注意力强化学习网络的派单算法在保证路径接近最优的同时, 求解时间满足即时派单需求. 网约车订单合乘和派单优化可以有效减少车辆的行驶距离和车队规模, 尽管订单合乘略微增加了乘客的等待时间, 但是在提高网约车的运营效率、缓解交通拥堵、减少环境污染等方面具有明显的促进作用.

This paper investigates the adaptive tracking control problem for Auto-Regressive Moving Average (ARMA) systems with quantized observations, explicitly focusing on reference signals composed of non-periodic sequences. The authors propose an adaptive tracking control scheme integrating an adaptive controller with a stochastic approximation-type estimation algorithm. Different from the control scheme for Finite Impulse Response (FIR) systems, the estimation part not only estimates the unknown system parameters but also the unknown system outputs. Next, based on the certainty equivalent principle, the adaptive controller is designed using the above two estimates instead of the actual parameters and system outputs. To tackle the inherent coupling between the two estimates, the authors introduce a novel approach that combines the Lyapunov function method with a backward-shifted polynomial method featuring time-varying coefficients. This approach assists in establishing the mean square convergence of the estimates with a convergence rate of $O\left(\frac{1}{k}\right)$ under suitable conditions of the step size coefficient. Additionally, this paper shows that the designed adaptive control law can achieve asymptotically optimal tracking of non-periodic reference signals in the mean square sense. Finally, a numerical simulation is presented to validate the theoretical results obtained in this paper.

In this paper, the authors address the problem of almost sure polynomial stabilization for a class of highly nonlinear stochastic systems via sampled-data feedback. The considered systems fall within a general framework that includes two key features: (a) Continuous-time irreducible Markov chain- the authors introduce a continuous-time irreducible Markov chain to describe systems that can undergo sudden alterations in their parameters and structures. This flexibility allows us to model real-world scenarios more accurately; (b) Diffusion and drift coefficients with polynomial growth - unlike existing literature that primarily focuses on systems with bounded delays, the authors investigate the stabilization conditions for highly nonlinear stochastic systems with pantograph delay, an unbounded delay. Specifically, the authors analyze systems where the diffusion and drift coefficients satisfy a polynomial growth condition. To achieve the proposed goal, the authors employ $M$-matrix theory and Lyapunov functions as basic tools. The main results establish that the system can attain almost sure polynomial stabilization through a subtly and innovatively designed sampled-data feedback. The authors validate the theoretical findings with numerical simulations, demonstrating the effectiveness of the proposed approach. This work contributes to the understanding of stabilization in highly nonlinear stochastic systems, particularly those with unbounded delays, and broadens the practical applicability of stochastic modeling.

To explore the impact of various groups and methods on rumor propagation, the authors propose a 'Double-Refutation $(DR)$ and Double-Blocking $(DB)$' rumor control strategy. This strategy combines external refutation via media reports, internal refutation by counteracting individuals, and both continuous and impulse blocking methods. By leveraging multi-synergy and aiming to minimize control costs, the authors propose stochastic optimal hybrid control strategies for rumor containment. Additionally, to enhance the response speed of the control strategy, the authors introduce an ensemble learning algorithm as a substitute for theoretical solutions. Numerical simulations demonstrate that the trained ensemble learning control algorithm can quickly identify sub-optimal control strategies for rumor spreading, with costs only 4.1% higher than those of the optimal control theory.

文章从拓扑结构的角度研究了基于一致性协议的符号多智能体网络的能控性.首先, 基于拉普拉斯矩阵的特征向量和领导者-跟随者结构, 得到了无向符号拓扑能控的代数充要条件. 依此条件, 对于通过连接两个子拓扑得到的合成拓扑, 提出了两种连接能控子拓扑来构造能控合成拓扑的方法. 此外, 基于不能控的无向符号拓扑,首次定义了同符号双重能控性破坏节点(same sign double controllability destructive nodes, SSDCDN)和逆符号双重能控性破坏节点(inverse sign double controllability destructive nodes, ISDCDN), 通过分析这些节点的特征得到了能控性在多领导者无向符号拓扑图上的充要条件. 最后, 在已有结论的基础上, 提出了两种特殊类型的不能控符号子拓扑与能控符号子拓扑连接形成能控合成符号拓扑的设计方法.

中国的政策实施和能源产业技术演变对于全球应对气候挑战具有非常显著的影响. 中国经济发展水平和资源禀赋分布在不同地区间存在一定“资源错配”现象, 由此导致中国能源产业技术发展的分布结构和演变路径也存在地区间差别. 文章基于全球-中国多区域综合评估模型WITCH-China, 量化研究选取的中国30个地区的能源产业技术发展及其碳排放的最优演变路径, 并对其能源产业技术优化演变进行综合评价. 研究发现: 一是政策情景下煤炭和石油消费呈现显著下降趋势, 风电和太阳能消费大幅上升, 非化石能源消费占比将在2050年首次超过化石能源; 二是碳排放量随着化石能源消费的下降而逐渐减少, 但由于能源效率的提升和清洁能源技术的普及, 碳排放量的下降幅度逐步趋缓; 三是不同地区在能源消费结构的变化趋势上存在明显差异, 清洁资源富集地区, 如青海、新疆、内蒙古、甘肃等, 其在清洁能源方面的发展潜力较大, 风电和太阳能的清洁替代潜力最为显著. 最后, 文章对中国能源产业发展及其最优路径提出相关政策建议.

文章提出一类误差自相关的空间变系数自回归模型. 该模型在空间变系数框架下, 同时引入响应变量的空间相关性与误差项的空间相关性, 共同刻画了空间数据的异质性与依赖性, 从而更全面地反映空间数据的复杂特征. 为克服模型的内生性问题, 融合局部线性估计、广义矩估计(GMM)和截面最小二乘估计方法, 提出一种有效的三阶段估计方法并推导了估计量的渐近性质. 蒙特卡罗模拟结果表明, 在有限样本下所研究模型的估计方法具有良好的有效性. 基于波士顿房价数据的实证分析进一步表明, 该模型显著提升了对空间经济现象的解释力.

This paper applies the Cheng projection to the support vector machine (SVM) in handling missing data. In the process of handling missing data, each sample with missing values is replaced by its Cheng projection in the original space. Additionally, two classification algorithms for handling linearly separable and nonlinearly separable datasets with missing data are presented. For linearly separable datasets with missing data, Cheng kernel function is introduced, and an SVM classification algorithm that improves the linear kernel function to the Cheng kernel function is proposed. For nonlinearly separable datasets, a generalized Gaussian Radial Basis Function kernel is introduced and an SVM classification algorithm for handling missing data is given. For both algorithms, two comparative experiments are conducted to demonstrate their effectiveness.

Both actuator faults and time delays degrade the performance of control systems. Although fault-tolerant mechanisms are commonly used in advanced control systems, no results are available in investigating the adaptive tracking problem of stochastic nonlinear time-delay systems in the presence of Markovian jump actuator faults. After establishing some mathematical fundamentals for stochastic differential delayed equations with multi-Markovian switching, this issue is tackled in this article, by proposing a novel adaptive backstepping fault-tolerant controller. Uncertainties caused by random actuator faults, unknown time-varying delays, the Wiener noise of unknown covariance as well as the unknown plant parameters are handled skillfully in a unified stochastic framework. By constructing a suitable Lyapunov-Krasovskii functional, it is proved that all closed-loop signals are bounded in probability, and the tracking error can converge into an arbitrarily small residual set in the sense of mean quartic value. In addition, the range of reference signals is greatly enlarged by comparison with the conventional backstepping controller. Two simulation examples are presented to illustrate the proposed theoretical findings.

In recent years, artificial cilia have attracted widespread research interest due to their enormous application prospects in the fields of medicine and environmental therapy. Deformation is a key issue to consider in the design and preparation of artificial cilia, however the corresponding mathematical analysis is still lacking. This paper introduces a multi-agent model for the magnetic artificial cilium, where each agent denoting a bead is influenced by the external magnetic field and neighboring agents. Then, the authors provide the existence and uniqueness of the solution to the proposed model, and give a stability condition for avoiding magnetic chain breakage and collisions between adjacent magnetic beads. To our best knowledge, it is the first mathematical result on the stability of magnetic bead chain. Finally, simulations are conducted to verify the proposed theoretical result.

This paper presents a primal-dual prediction-correction (PD-PC) method for solving linearly constrained time-varying convex optimization problems, which frequently arise in control, signal processing, and online learning applications. The proposed method establishes a novel integration of primal-dual gradient dynamics with a discrete-time prediction-correction structure, specifically designed for problems with time-dependent linear constraints. A tunable memory parameter is introduced in the prediction phase to perform linear extrapolation using past iterates, enabling a flexible trade-off between the amount of historical information stored and the computational cost of correction. In the correction phase, primal and dual variables are updated via gradient descent-ascent iterations, thus maintaining the computational efficiency of a first-order method without requiring Hessian or high-order derivative computations. Theoretical analysis shows that the method achieves $\mathcal{O}(h^2)$ asymptotic tracking accuracy for both primal and dual variables, matching the state-of-the-art performance among first-order methods even in unconstrained settings. Numerical experiments on problems with both time-invariant and time-varying constraints validate the theoretical findings and demonstrate the method's effectiveness.

The cooperative output regulation problem for unknown linear multi-agent systems has been studied by both policy-iteration method and value-iteration method via distributed internal model approach. However, the original results were limited to single-input single-output linear multi-agent systems under the assumption that the communication digraph is acyclic. Recently, the authors have extended the existing result to multi-input multi-output linear multi-agent systems over a general static and connected digraph by a more efficient value-iteration method. Since the policy-iteration method is simpler and has a much faster convergence rate than the value-iteration method, in this paper, the authors further apply the policy-iteration method to the cooperative output regulation problem of unknown multi-input multi-output multi-agent systems over a general static and connected digraph. Compared with the existing policy-iteration method, the proposed policy-iteration approach not only drastically reduces the computational cost, but also significantly weakens the solvability conditions. Moreover, by introducing a virtual exosystem, the proposed policy-iteration approach eliminates the need for employing a distributed observer. As a result, the data collection can start at any time, and the computing cost for each agent is also reduced.

利用2009年至2023年华证ESG评级数据, 文章检验上市公司ESG评级对企业下一年度在股价、财务和经营三个方面的风险水平的影响. 研究发现, 较好的ESG评级对企业未来一年三项风险水平均有显著的抑制作用. 具体地, 对于股价崩盘风险, 高于基准水平的ESG评级作为一种强烈的市场信号对企业股价崩盘风险水平的降低更显著; 投资者关注程度高的个股, ESG降低企业股价崩盘风险作用更强; 此外, 在《环境保护法》实施后ESG和股价崩盘风险的负向关系更加显著. 而对于财务风险, ESG对于企业财务风险的降低具有边际递减效应, ESG评级从低到中的提高能够改善企业财务风险水平, 但随着ESG评级进一步的提高, 该作用变得不显著; 同时,企业自愿披露非财务信息行为强化了 ESG 对于财务风险的抑制作用. 对于经营风险, ESG评级降低经营风险作用同样具有边际递减效应; 同时股权性质对ESG评级降低经营风险有调节作用, 相比于私企, ESG评级对国有企业经营风险的抑制作用更强. 文章基于企业年限长短的分样本异质性检验结果显示, 对于成立年限长的企业, ESG评级对风险的抑制作用更强, 而对于成立年限短的企业则较弱.

文章基于东方财富股吧论坛投资者行为数据, 利用有效转移熵方法, 探讨股吧论坛在股票市场中的媒介作用更能表征投资者关注还是情绪. 研究发现: 1) 股吧论坛中的投资者关注向股票市场传递的信息流更有价值, 能够更精准地表征投资者行为; 2) 帖子净评论数作为投资者关注的重要指标, 与股票市场强相关, 显著影响个股波动率, 且这一影响可持续一个月; 3) 在不同情绪(积极、中立、消极)的帖子中, 投资者关注均能传递可预测个股波动率的信息流, 而投资者情绪向市场传递的信息流缺乏显著的信息增益价值. 文章进一步强调了股票论坛捕捉投资者关注的媒介作用, 特别是帖子净评论数的独特预测价值, 为投资者提供了分析在线股票论坛数据的新视角. 未来研究应聚焦于更全面的数据整合与深度挖掘, 以提升市场预测的准确性和投资决策的科学性.

文章关注汽车充电服务供给端, 构建以政府、运营商、第三方平台(下称平台)为主体的分散(即运营商和平台以自身利益最大化为目标)和集中(即运营商和平台以二者整体利益最大化为目标)两种充电服务运营决策微分博弈模型. 研究发现: 在收益分配比例固定的均衡状态下, 相较于分散决策模式, 集中决策模式可提高运营商与平台的努力程度、服务质量及社会效益; 当平台较为弱势而收益分配占比较低时, 采用集中决策模式可实现运营商和平台收益的帕累托改进; 当平台较为强势而收益分配占比较高时, 采用分散决策模式可使运营商和平台收益增加. 这表明随着平台由弱变强, 汽车充电服务供给的决策模式或将从集中转向分散, 此时政府的政策支持比例将提升, 社会效益和服务质量反而下降.

文章针对一阶线性不确定系统,研究了以饱和函数和以ReLU函数为激活函数的循环神经网络控制器的稳定性和鲁棒性.给出了闭环系统收敛到非零目标值的必要条件以及指数稳定的充分条件,定量刻画了状态初值、目标值以及对象未知参数鲁棒性范围与控制器参数之间的关系,分析结果表明,以ReLU函数为激活函数的RNN控制器的鲁棒性更强.

注册制是中国资本市场发展的关键改革,推行询价制改革,旨在将定价权更多交由市场主体,提升新股定价效率.因此,围绕注册制下的IPO定价效率评价和询价制改革效果的系统性研究备受关注.文章基于双边随机前沿模型测度2016–2023年A股2365家上市公司IPO定价效率,并验证注册制下询价制改革对IPO定价的系统性影响.研究发现,2016–2023年A股市场首发定价中抑价效应强于抬价效应,整体定价低于合理水平7.06%,且在不同板块、年份、所有制形式和破发与否的条件下有其明显特征区别.询价制改革整体推高了股票首发定价,主要由股票市场内科创板依靠板块特性独自拉动,而其他板块则呈现下降趋势.在注册制推广阶段,科创板和创业板存在明显竞争,主板改革效果并不明显.同时,梳理出询价机构数量和报价有效性两种路径来解释其影响.最终,文章依据研究结论为监管部门询价制改革方向提出针对性建议.

稀疏性是高维建模过程中一个重要的假设,即在处理高维回归分析中,往往只有少部分变量对响应变量具有显著影响,这便是稀疏性的体现.文章在变系数模型的基础上,结合变量选择,针对纵向数据提出具有混合效应的变稀疏系数分位回归(VSCMEQ)模型,其中待估系数函数使用B样条估计,通过对随机效应和固定效应施加惩罚来研究相关重要因素(变化效应、常数效应)的影响.最后通过应用原发性胆汁性肝硬化(PBC)数据进行建模分析,得出在不同分位数下对患者疾病进展(生物指标)的重要因素影响.

文章考虑由两个供应商和一个零售商构成的供应链,其中一个供应商为供销一体型,另一个为纯供应商.在该系统中,零售商从纯供应商进行补货并在其供应中断时从供销一体型供应商进行紧急补货.文章主要讨论在纯供应商发生供应中断且供应中断结束时间随机情形下零售商的紧急补货决策和该情形下供销一体型供应商的零售价格决策问题.对此,基于供应链各成员效益最大化,建立了一个优化模型,并通过模型分析给出了模型的最优解,从而给出了供应链各成员的最优决策.数值实验讨论了主要参数对零售商紧急补货量和供应商零售价格及双方效益的影响.

趋势滤波是一种广泛应用于从时间序列数据中提取长期趋势和去除短期噪声的方法.为了准确捕捉潜在趋势的全局变化规律和局部波动,文章基于刻画稀疏性的本原函数提出带复合$\ell_0$约束的广义趋势滤波(L0CTF)模型并分析其最优性理论.复合$\ell_0$函数的组合属性和不可分性导致求解L0CTF模型是一项极具挑战的任务.为此,基于复合$\ell_0$约束的特点,文章将L0CTF模型转化为含第一类特殊有序集的混合整数规划问题并分析其与L0CTF模型在全局最优解意义下的等价性.最后,在模拟数据集和真实数据集上的实验结果表明,所提方法在提取潜在趋势方面优于主流的趋势滤波方法.

在工程实际中,通常难以获得单个部件的精确失效时间,相反地,仅仅知道部件失效的次数和系统运行的总时长,即聚合寿命数据.基于此种数据对寿命分布进行推断是十分具有挑战性的.文章提出了基于矩的点估计方法,并采用偏差校正Bootstrap方法来构建感兴趣量的置信区间.极大似然方法需要写出聚合寿命数据的似然函数,这仅对少数几个具有卷积运算封闭性质的分布才适用.与之相比,所提方法规避了似然函数,因此能适用于更多的分布类型.最后,文章通过模拟研究和实证分析对所提方法进行了验证和说明.

本文旨在探讨时谐波动方程反源问题的一些研究进展,并建立一般情形下反源问题的稳定性理论.我们针对确定和随机波动方程的散射模型,总结已有理论和数值结果并阐明研究思路.

为了对冲突事件进行结构化表示,识别事件的逻辑关系,发现事件演变规律和影响事件发展的关键节点,文章基于在线媒体新闻数据构建标准语料库,使用融合注意力机制和依存句法分析的图神经网络解决冲突事件抽取存在的事件要素长距离依赖问题,使用基于RoBERTa的fine-tuning方法解决隐性事件关系识别问题,提出冲突事件本体模型、冲突事件知识图谱的自动化构建框架和关键路径识别算法.结果表明,冲突事件识别算法SGAEE和基于RoBERTa微调的事件关系识别算法均优于对比算法,覆盖重要节点的最少路径识别算法MPCCN可以识别出影响冲突事件发展的关键事件和关键路径.文章提出的冲突事件知识图谱构建方法可以实现对冲突事件进行多层次和多视角的分析,帮助专家掌握冲突事件发展脉络,提高决策的全面性和科学性.

在火工品可靠性设计和评估中,高效准确地估计感度分布的参数及分位数至关重要.文章采用贝叶斯方法,建立感度数据的半参数广义线性模型,利用Hamiltonian Monte Carlo算法进行后验推断,并借助偏差信息准则和对数伪边际似然,以数据驱动的方式筛选“最优"模型.模拟比较验表明所提方法在小样本情况下可以获得感度分布参数的准确估计.最后,实际数据分析验证了新方法的有效性,并为感度数据的分析提供了一种替代和补充的建模工具.

Duadic码是一类重要的循环码,构造最小距离具有平方根下界的duadic码是一个有趣的研究问题.文章提出了两种构造最小距离具有平方根下界的odd-like duadic码的构造方法,得到两类最小距离具有平方根下界的odd-like duadic码.

The authors extend the marginal coordinate test for predictor contribution (Cook, 2004) to the case with multivariate responses. Instead of explicitly specifying the link functions between the responses and the predictors, an asymptotic test is proposed under the normality assumption of the predictors as well as an asymmetry assumption about the unknown regression mean function. When these assumptions are violated, the asymptotic test with elliptical trimming and clustering is still valid with desirable numerical performances.

This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain. This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions. Traditional regression model checks and two-sample regression tests are insufficient to address this issue. To overcome these limitations, the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free. Under the null hypothesis, the test follows a chi-square weak limit, preserving the significance level and enabling critical value determination without resampling techniques. Additionally, the authors systematically analyze the test’s power performance, highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis. Numerical studies, including simulations, assess finite-sample performance, and a real-world data example is provided for illustration.

液压作动器广泛应用于工业控制系统中, 其精确的位移控制对系统性能至关重要. 传统的物理建模方法难以准确捕捉液压作动器的非线性和时序特性, 限制了其在复杂环境下的应用. 文章提出了一种基于长短期记忆(LSTM)神经网络的液压作动器位移建模方法. 通过采集电压输入和位移输出的时序数据, 使用LSTM网络对液压作动器的动态行为进行建模. LSTM网络能够有效捕捉数据中的长期依赖关系, 适应液压系统的非线性时序特性. 在模型训练过程中, 采用均方误差作为优化目标, 并通过实验验证了LSTM网络的有效性. 实验结果表明, 相较于传统方法LSTM网络在测试集上取得了更低的预测误差, 具有更强的建模能力和较高的精度.

自创区作为引领创新发展的重要引擎, 以体制机制改革和政策先行先试为出发点, 辐射带动周边区域创新协调发展. 高铁网络的逐步完善为“双循环”打开新格局, 扩大了自创区创新溢出作用的范围. 文章基于2008-2019年中国高铁城市对数据, 采用多期双重差分法, 系统研究开通连接到自创区城市的高铁后对普通城市创新水平的提升效果和作用机制. 研究发现, 开通连接到自创区城市的高铁后能显著提高普通城市创新水平, 自创区城市的创新溢出作用对东部地区城市、创新环境较好的城市和大规模城市的效果更明显. 机制检验表明, 自创区城市的创新溢出效应通过利用创新禀赋、政府引导创新和示范带动效应三个方面实现. 文章为高铁网络化背景下自创区城市发挥示范带动作用, 进一步优化创新资源空间配置和加快发展新质生产力, 实现经济高质量发展提供了经验证据和政策启示.

当今时代,数据资源日益呈现出多源化的特点,即数据由不同的主体或来源方所持有.对多源数据进行融合开发是激活数据要素潜能、释放数据资源价值的前提和基础.然而,大部分多源数据仍然以“数据孤岛”的形式存在,数据间的互联互通仍然十分困难.与此同时,数据安全问题备受关注,如何在保护数据隐私的前提下实现多源数据的安全开发变得尤为重要.为了解决上述问题,文章提出了一种基于隐私保护的多源数据分析范式.该方法依托纵向联邦学习框架,允许各数据持有方在不暴露各自原始数据的前提下协作完成数据分析任务.与此同时,为了进一步防止攻击者对数据的恶意攻击, 文章在纵向联邦学习框架中引入差分隐私,通过在传输信息时加入噪声以保护个体隐私.最后,文章以工商管理中的企业违规风险预测为例展示了上述方法的实际应用效果,通过结合工商部门、市场监督管理部门、法院等多个部门的数据,提升了企业违规风险预测的准确性.

In this paper, disturbance attenuation is considered for linear systems with partially modeled disturbance. The disturbance signal is composed of known signals and uncertain parameters that leads to some difficulties for solving the disturbance rejection problem. To overcome this issue, the original system is reformulated as a linear parameter-varying (LPV) system by absorbing the unknown parameters in disturbance. Then an adaptive state-disturbance-feedback controller relying on a dictionary of state-feedback gains and disturbance-feedback gains is designed to estimate the uncertain parameters in the LPV system. Moreover, the presence of multiple variables in the sufficient condition given to reject the external disturbance of the LPV system also brings challenges. To tackle this problem, the quadratic separation technology is applied into the sufficient condition, and the original unsolvable condition can be successfully transferred into a solvable one. Furthermore, by adding the known part of the disturbance signal into the feedback loop, more information of the whole system can be utilized. Meanwhile, the asymptotical stability of the closed-loop system can be achieved and the $H_\infty$ performance index of the closed-loop system is verified to be smaller. Numerical simulations are given to illustrate the merits of the proposed approach.

The authors consider a two-period joint inventory and pricing decision problem for a retailer facing strategic customers with behavioral preferences such as reference dependence, loss aversion and risk preferences. The authors develop and analyze a model that accounts for customers' these behavioral preferences as well as value depreciation on the product, and makes predictions on the retailer's optimal decisions. Moreover, the authors demonstrate how the presence of these behavioral preferences and primary parameters will leverage the retailer's optimal decisions. It is revealed that strategic customers' loss aversion behavior could benefit the retailer from pushing up the regular price, the stocking quantity and hence the expected profit. However, customer's value depreciation on the product will drive down these aspects. To alleviate the negative effect of the strategic customers' behavioral preferences, the authors suggest the retailer applying inventory commitment strategy and price guarantee policy, which could increase the retailer's profit beyond the rational expectation equilibrium level in some situations.