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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
PD Controller: Design01:26

PD Controller: Design

353
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
353
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
87
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

181
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
181

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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基于ICEEMDAN-MPE-PSO-DELM模型的短期交通流量预测研究

Xiujuan Tian1, Jinyong Ding1, Huanying Liu2

  • 1School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, 130118, Jilin, China.

Scientific reports
|July 19, 2025
PubMed
概括

本研究引入了一种使用先进的分解和组合方法的新型短期交通流量预测模型. 拟议的模型显著提高了交叉路口的交通流量预测准确度.

关键词:
经验模式分解分解混合预测可以预测.改进了深度极端学习的机器学习.多个尺度的顺序 entropy.交通流量预测和预测

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科学领域:

  • 交通工程是交通工程.
  • 人工智能的人工智能
  • 时间序列分析时间序列分析

背景情况:

  • 准确的短期交通流量预测对于智能交通系统至关重要.
  • 现有的模型经常与交叉点的交通流动的复杂,非线性动态作斗争.

研究的目的:

  • 提出一种新的混合模型,以提高十字路口的短期交通流量预测准确度.
  • 利用数据分解和集体学习来提高预测性能.

主要方法:

  • 实证模式分解 (EMD) 变体,如ICEEMDAN用于时间序列分解.
  • 具有粒子群优化 (PSO) 的多尺度转换 (MPE) 来评估组件的随机性.
  • 使用基于组件随机性的深度极端学习机器 (DELM) 和ARIMA模型进行混合预测.

主要成果:

  • 拟议的ICEEMDAN-MPE-PSO-DELM-ARIMA模型表现出了卓越的性能.
  • 与其他基准模型相比,实现了最小的预测错误.
  • 与实际的交通流量值表现出最好的匹配效果.

结论:

  • 新的混合模型有效地提高了短期交通流量预测的准确性.
  • 分解,随机性评估和整体预测的结合是非常有效的.
  • 该模型为智能交通管理提供了一个有前途的解决方案.