Continuous Time Model(连续时间模型)研究综述
Continuous Time Model 连续时间模型 - In biomedical studies, these stochastic continuous-time models are used to describe the time-to-event life history of an individual through a flexible framework for longitudinal data. [1] Firstly, through a model transformation, the original continuous-time model is transformed into an identifiable differential form. [2] The day-to-day dynamics under the two above-mentioned scenarios are formulated using both discrete-time and continuous-time models, and their respective local stability is analyzed. [3] We follow them for three years and estimate their moving patterns using multinomial logistic regressions and continuous-time models that account for the lagged effect of separation. [4] We cover both discrete and continuous-time models. [5] However, whether and how the sampling rate affects the quality with which such continuous-time models can be estimated, has largely been ignored in the literature. [6] In this paper, we consider a stochastic model of spatio-temporal propagation of BLSD in an heterogeneous landscape and we present mathematical and computational results for this continuous-time model. [7] On top of that, their continuous-time model takes the mathematical form of a renewal equation, and only a few experts can handle such equations numerically. [8] The paper is devoted to stability analysis of discrete-time time-delay systems, obtained after explicit Euler discretization of (locally) homogeneous continuous-time models. [9] Equivalent continuous-time model, proposed account. [10] In this paper, by using zeroing neural dynamics method, a continuous-time model is proposed for solving the time-varying problem of QRD in real-time. [11] To take into account the typically continuous-time modeling of biological species and, instead, of the specialized harvesting activities, which by its nature cannot change continuously, the resulting dynamic system is of the hybrid type, i. [12] This paper presents the design of a velocity form for spline-based continuous-time model predictive control to achieve offset-free tracking of piecewise constant reference signals in the presence of disturbances. [13] It is shown analytically that the discrete-time technique is able to maintain the uniqueness of equilibrium point and its dynamic behavior of the continuous-time model under the same conditions. [14] The machine learning algorithms used in this paper are Logistic Regression (LR), Support Vector Machine (SVM), Feed forward neural network (FFN) and Recurrent Neural Network (RNN) while continuous-time models such as Stochastic Differential Equation (SDE) and Geometric Brownian Motion (GBM) are also used. [15] However, for many complex systems, it is useful to develop continuous-time models of networks and to compare them to associated discrete models. [16] output voltage, current- and voltage ripple, was assumed based on a continuous-time model, including power losses. [17] We use this formalism to compare the scattering amplitudes of a continuous-time model and of the corresponding discretized one. [18] We show how interventions from Pearls do-calculus can be translated from static to continuous-time models. [19] These analytical expressions were obtained considering a continuous-time model for the GFDM signal and modeling the underwater acoustic communication channel as a time-varying multipath channel. [20] Continuous-time modeling allows to explore this interval dependence of cross-lagged effects and thus to identify the maximal “peak” cross-lagged effects. [21] To address this challenge, the nonlinear continuous-time model is linearized and discretized in time by the use of Cayley-Tustin transform without spatial discretization or model reduction. [22] We develop a game-theoretic, continuous-time model of the leveraged firm under Chapter 11 to assess the wealth transfers and welfare impacts of such an amendment to the bankruptcy procedure. [23] this discrete-space and continuous-time model admits regular temporal patterns since the delay induces Hopf bifurcations with network structure. [24] In this technical communique, a locally convergent continuous-time model for the Durand–Kerner method is proposed to simultaneously determine all the roots of a time-varying polynomial. [25] In a simple continuous-time model where the learning process affects the willingness to hold liquidity, we provide an intuitive explanation of business cycle asymmetry and post-crisis slow recovery. [26] Specifically, to break the limitations of existing continuous-time models in handling nonstationary problems, a discrete recurrent neural dynamics model is proposed to robustly deal with noise. [27] In this paper, one “$\mathscr{L}_{1}$ adaptive controller $({\mathscr{L}_{1}}\_{\text{AC}})$” using “continuous-time model predictive control (CMPC)” is proposed on position tracking and removing vibration and deviation in “single-link flexible joint manipulator (SLFJM)” with existence of the unknown nonlinear dynamics and uncertainties. [28] The temporal networks generated by our open-loop design are versatile in the sense of promoting synchronization for systems with vastly different dynamics, including periodic and chaotic dynamics in both discrete-time and continuous-time models. [29] This paper proposes the new grid bootstrap to construct confidence intervals (CI) for the persistence parameter in a class of continuous-time models. [30] We develop a continuous-time model for β-amyloid aggregation using concepts from chemical kinetics and population dynamics. [31] Mathematical tools and methods needed to qualitatively analyze deterministic continuous-time models, formulated by ordinary differential equations, are also introduced, while its discrete-time counterparts are properly referenced. [32] We establish a continuous-time model to solve the optimal consumption-saving problem with life annuity. [33] By applying Zhang neural dynamics method, this study proposed, analyzed, and investigated a continuous-time model for solving the time-varying Schur decomposition (SD) problem. [34] In many engineering applications, continuous-time models are preferred to discrete-time ones, in that they provide good physical insight and can be derived also from non-uniformly sampled data. [35] ABSTRACT Continuous-time models generally imply a stochastic differential equation for latent processes, coupled to a measurement model. [36] The local dynamics with different topological classifications, bifurcation analysis, and chaos control for the phytoplankton–zooplankton model, which is a discrete analogue of the continuous-time model by a forward Euler scheme, are investigated. [37] Here, we show how to derive and to compute the transfer function for a continuous-time model of a population that is structured by a continuous individual-level state variable such as size. [38] Then, we extend existing frameworks for aggregation-based analysis of Markov chains by allowing them to handle chains with an arbitrary structure of the underlying state space – including continuous-time models – and improve upon existing bounds on the approximation error. [39] It is shown that an appropriately chosen continuous-time model of a digital controller with the PWM power converter behaves like the actual discrete-time system, which allows for a simple controller analysis and design. [40] This work is motivated by the increasing complexity of continuous-time models and stylized facts reported in the literature. [41] Motivated by our theoretical model, we adopt a continuous-time model with a random and time-varying persistence parameter to empirically investigate the presence of speculative bubbles in daily oil future prices over the period April 1983 to June 2020. [42] We build a parsimonious continuous-time model for longevity risk that captures the dependence across different ages in domestic versus foreign populations. [43] A simplified approach which ignores distant time effects identifies an optimal ‘time to deliver’ and an optimal ‘amount to deliver’ for a production process run in continuous time modelled by a Cobb-Douglas revenue function. [44] The continuous time models are frequently used by researchers and practitioners are like in behavioral and in a host of related domains. [45] The control methodology is developed in discrete time, however, is validated by numerical simulations using a continuous time model. [46] The predictive control model (MPC) is based on the discretization of the continuous time model of the system to have a discrete time model that allows us to predict the future value of the controlled variable in different possible control situations, and concerning the cost function defined by user to ensure the control objectives decided to find its minimum. [47] In conventional discrete time methods, the robust stability is not directly prescribed or available systems are restricted to systems for which the dead-time in the continuous time model is an integer multiple of the sampling interval. [48] The simulation of the continuous time model and its sensitivity propagation are key tasks within any standard NMPC algorithm and have to be carried out frequently. [49] In this paper, the mathematical model of open-pit mines vehicle scheduling problem using continuous time modeling is established. [50]在生物医学研究中，这些随机连续时间模型用于通过纵向数据的灵活框架来描述个体的时间到事件的生活史。 [1] 首先，通过模型变换，将原来的连续时间模型变换为可识别的微分形式。 [2] 使用离散时间和连续时间模型制定了上述两种情景下的日常动态，并分析了它们各自的局部稳定性。 [3] 我们跟踪它们三年，并使用多项逻辑回归和连续时间模型来估计它们的移动模式，这些模型解释了分离的滞后效应。 [4] 我们涵盖了离散和连续时间模型。 [5] 然而，采样率是否以及如何影响可以估计这种连续时间模型的质量，在文献中很大程度上被忽略了。 [6] 在本文中，我们考虑了 BLSD 在异质景观中的时空传播的随机模型，并给出了该连续时间模型的数学和计算结果。 [7] 最重要的是，他们的连续时间模型采用了更新方程的数学形式，只有少数专家能用数值处理这样的方程。 [8] 本文致力于离散时间延迟系统的稳定性分析，该系统是在（局部）齐次连续时间模型的显式欧拉离散化后获得的。 [9] 等效连续时间模型，建议帐户。 [10] 本文采用归零神经动力学方法，提出了一种实时求解QRD时变问题的连续时间模型。 [11] 考虑到生物物种的典型连续时间建模，而不是专门的收获活动，其本质上不能连续变化，由此产生的动态系统是混合类型的，即。 [12] 本文介绍了基于样条的连续时间模型预测控制的速度形式的设计，以在存在干扰的情况下实现分段常数参考信号的无偏移跟踪。 [13] 分析表明，离散时间技术能够保持平衡点的唯一性及其在相同条件下连续时间模型的动态行为。 [14] 本文使用的机器学习算法有逻辑回归（LR）、支持向量机（SVM）、前馈神经网络（FFN）和循环神经网络（RNN），而连续时间模型如随机微分方程（SDE）和还使用几何布朗运动 (GBM)。 [15] 然而，对于许多复杂系统，开发网络的连续时间模型并将它们与相关的离散模型进行比较是有用的。 [16] 输出电压、电流和电压纹波是基于连续时间模型假设的，包括功率损耗。 [17] 我们使用这种形式来比较连续时间模型和相应离散模型的散射幅度。 [18] 我们展示了如何将 Pearls do-calculus 的干预从静态模型转换为连续时间模型。 [19] 这些解析表达式是在考虑 GFDM 信号的连续时间模型并将水声通信信道建模为时变多径信道的情况下获得的。 [20] 连续时间建模允许探索交叉滞后效应的这种区间依赖性，从而确定最大“峰值”交叉滞后效应。 [21] 为了应对这一挑战，非线性连续时间模型通过使用 Cayley-Tustin 变换在时间上进行线性化和离散化，而无需空间离散化或模型缩减。 [22] 我们根据第 11 章开发了杠杆公司的博弈论、连续时间模型，以评估这种破产程序修正案的财富转移和福利影响。 [23] 这种离散空间和连续时间模型允许规则的时间模式，因为延迟会导致具有网络结构的 Hopf 分岔。 [24] 在本技术公报中，提出了一种用于 Durand-Kerner 方法的局部收敛连续时间模型，以同时确定时变多项式的所有根。 [25] 在一个简单的连续时间模型中，学习过程影响持有流动性的意愿，我们提供了商业周期不对称和危机后缓慢复苏的直观解释。 [26] 具体来说，为了打破现有连续时间模型在处理非平稳问题方面的局限性，提出了一种离散递归神经动力学模型来稳健地处理噪声。 [27] 在本文中，一个使用“连续时间模型”的“$\mathscr{L}_{1}$自适应控制器$({\mathscr{L}_{1}}\_{\text{AC}})$”针对存在未知非线性动力学和不确定性的“单连杆柔性关节机械手（SLFJM）”中的位置跟踪和消除振动和偏差，提出了预测控制（CMPC）。 [28] 我们的开环设计生成的时间网络在促进具有截然不同动力学的系统的同步方面是通用的，包括离散时间和连续时间模型中的周期性和混沌动力学。 [29] 本文提出了新的网格引导程序来为一类连续时间模型中的持久性参数构建置信区间 (CI)。 [30] 我们使用来自化学动力学和群体动力学的概念开发了β-淀粉样蛋白聚集的连续时间模型。 [31] 还介绍了定性分析由常微分方程表示的确定性连续时间模型所需的数学工具和方法，同时适当引用了其离散时间对应物。 [32] 我们建立了一个连续时间模型来求解人寿年金的最优储蓄问题。 [33] 本研究应用张神经动力学方法，提出、分析和研究了求解时变舒尔分解（SD）问题的连续时间模型。 [34] 在许多工程应用中，连续时间模型优于离散时间模型，因为它们提供了良好的物理洞察力，并且还可以从非均匀采样的数据中导出。 [35] 摘要 连续时间模型通常意味着潜在过程的随机微分方程，耦合到测量模型。 [36] 研究了浮游植物-浮游动物模型的不同拓扑分类、分岔分析和混沌控制的局部动力学，该模型是前向欧拉格式的连续时间模型的离散模拟。 [37] 在这里，我们展示了如何推导和计算人口的连续时间模型的传递函数，该模型由连续的个体水平状态变量（例如大小）构成。 [38] 然后，我们扩展了现有的基于聚合的马尔可夫链分析框架，允许它们处理具有基础状态空间的任意结构的链——包括连续时间模型——并改进近似误差的现有界限。 [39] It is shown that an appropriately chosen continuous-time model of a digital controller with the PWM power converter behaves like the actual discrete-time system, which allows for a simple controller analysis and design. [40] 这项工作的动机是连续时间模型和文献中报道的程式化事实的日益复杂。 [41] 在我们的理论模型的启发下，我们采用具有随机和时变持久性参数的连续时间模型来实证研究 1983 年 4 月至 2020 年 6 月期间每日石油期货价格中投机泡沫的存在。 [42] 我们为长寿风险建立了一个简洁的连续时间模型，该模型捕捉了国内与国外人群不同年龄的依赖性。 [43] 一种忽略遥远时间影响的简化方法确定了最佳“交付时间”和最佳“交付量”，用于以 Cobb-Douglas 收益函数建模的连续时间运行的生产过程。 [44] 研究人员和从业者经常使用连续时间模型，就像在行为和许多相关领域一样。 [45] 控制方法是在离散时间开发的，但是通过使用连续时间模型的数值模拟来验证。 [46] 预测控制模型 (MPC) 是基于系统连续时间模型的离散化，具有离散时间模型，使我们能够预测受控变量在不同可能的控制情况下的未来值，并涉及定义的成本函数由用户确保控制目标决定找到其最小值。 [47] 在传统的离散时间方法中，鲁棒稳定性不是直接规定的，或者可用系统仅限于连续时间模型中的死区时间是采样间隔的整数倍的系统。 [48] 连续时间模型的模拟及其灵敏度传播是任何标准 NMPC 算法中的关键任务，并且必须经常执行。 [49] 本文利用连续时间建模建立了露天矿山车辆调度问题的数学模型。 [50]
discrete time model 离散时间模型
For a variety of purposes, discrete-time models are superior to continuous-time models. [1] The solution carries structural properties analogous to those obtained under continuous-time models, and it provides a useful tool for making new discoveries through discrete-time models. [2] Key Findings ▪ For the term structure of interbank rates in the UK, Europe, and Japan, more complex continuous-time models that include more factors are superior in terms of predictive power to models with less factors or discrete-time models. [3] In terms of social welfare maximization, we show that the continuous-time model is superior to the discrete-time model. [4] We examine the connection between discrete-time models of financial markets and the celebrated Black--Scholes--Merton (BSM) continuous-time model in which "markets are complete. [5] We systematically make comparisons of the dynamical behaviors between the discrete-time model and the continuous-time model to study the robustness of model predictions to time discretization. [6] In this letter, we exploit key differences between the continuous-time model of the first-order Approximate Differentiator, commonly known as the Dirty-derivatives Filter (DF), and its discrete-time model. [7] The result holds for discrete-time models that are originated from continuous-time models by means of discretization. [8]出于各种目的，离散时间模型优于连续时间模型。 [1] 该解决方案具有类似于在连续时间模型下获得的结构特性，并且它为通过离散时间模型获得新发现提供了有用的工具。 [2] 主要发现 ▪ 对于英国、欧洲和日本的银行同业拆借利率期限结构，包含更多因子的更复杂的连续时间模型在预测能力方面优于包含较少因子的模型或离散时间模型。 [3] 在社会福利最大化方面，我们表明连续时间模型优于离散时间模型。 [4] 我们研究了金融市场的离散时间模型与著名的 Black-Scholes-Merton (BSM) 连续时间模型之间的联系，其中“市场是完整的。 [5] 我们系统地比较了离散时间模型和连续时间模型之间的动力学行为，以研究模型预测对时间离散化的鲁棒性。 [6] 在这封信中，我们利用一阶近似微分器的连续时间模型（通常称为脏导数滤波器 (DF)）与其离散时间模型之间的关键差异。 [7] nan [8]