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相关概念视频

State Space Representation01:27

State Space Representation

205
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
205
State Space to Transfer Function01:21

State Space to Transfer Function

198
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
198
Transfer Function to State Space01:23

Transfer Function to State Space

247
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
247
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81
Multimachine Stability01:25

Multimachine Stability

151
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
151
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106

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相关实验视频

Updated: Jun 27, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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动态层次状态空间预测空间预测

Ziyue Liu1, Wensheng Guo2

  • 1Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana.

Statistics in medicine
|May 1, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的时间序列预测方法,该方法利用来自相似单位的数据和目标单位的历史. 这种方法通过整合共享模式和单元特定数据来改善预测,以便准确预测.

关键词:
根据COVID-19的情况,预测 预测 预测 预测内部时间 内部时间国家空间模型.时间序列时间序列

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科学领域:

  • 统计 统计 统计 统计
  • 流行病学 流行病学
  • 计算科学 计算科学

背景情况:

  • 准确的时间序列预测对于公共卫生和资源分配至关重要.
  • 现有的方法往往难以有效地结合外部信息和单位特定的历史数据.
  • 了解疾病动态需要能够适应不断变化的模式的模型.

研究的目的:

  • 开发一种新的时间序列预测模型,借鉴相关单位的信息,同时结合目标单位的历史.
  • 通过使用由内部时间指数对齐的共享模式来提高预测的准确性.
  • 为在多个相关时间序列的情况下提供灵活的预测框架.

主要方法:

  • 为多个时间序列数据构建了一个层次状态空间模型.
  • 开发了一个条件状态空间模型,以整合外部单元信息作为先前知识.
  • 卡尔曼过被应用到条件状态空间模型进行预测.
  • 预测从内部时间转换为日历时间,以确保可解释性.

主要成果:

  • 拟议的模型有效地结合了现有单位的信息和目标单位的历史.
  • 模拟研究表明了该方法的有限样本性能.
  • 该方法在预测美国州级新增COVID-19病例方面表现有前途.

结论:

  • 开发的方法提供了一种有效的方式,通过利用跨单位信息来增强时间序列预测.
  • 这种方法为流行病学预测和其他具有多个相关时间序列的应用提供了一个强大的框架.
  • 集成内部时间对齐和层次建模,在预测准确度方面取得了重大进展.