Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

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

50
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...
50

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Range-Aware Attention Framework for Meteorological Visibility Estimation.

Sensors (Basel, Switzerland)·2026
Same author

YOLO-ERCD: An Upgraded YOLO Framework for Efficient Road Crack Detection.

Sensors (Basel, Switzerland)·2026
Same author

Advancing Biomechanics-Based Motion Analysis from Methodology to Application.

Bioengineering (Basel, Switzerland)·2025
Same author

Biomechanics and Motion Analysis: From Human Performance to Clinical Practice.

Bioengineering (Basel, Switzerland)·2025
Same author

Determining post-orthognathic surgery profile: A validation study on the potential of modified chin point as a reliable predictor.

PloS one·2025
Same author

Evaluation of Chatbot Responses to Text-Based Multiple-Choice Questions in Prosthodontic and Restorative Dentistry.

Dentistry journal·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 25, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.5K

通过使用粒子集群优化和人工神经网络进行光伏面板模型参数估计.

Wai-Lun Lo1, Henry Shu-Hung Chung2, Richard Tai-Chiu Hsung1

  • 1Department of Computer Science, Hong Kong Chu Hai College, Hong Kong, China.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,用于使用人工神经网络 (ANN) 和粒子群优化 (PSO) 估计光伏 (PV) 面板参数. 该方法提高了光伏面板健康监测和最大功率点跟踪的准确性和融合速度.

关键词:
模型参数估计模型参数估计神经网络的神经网络的神经网络粒子群集优化 粒子群集优化太阳能电池板的光伏组件

更多相关视频

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

9.9K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

相关实验视频

Last Updated: Jun 25, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.5K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

9.9K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

科学领域:

  • 可再生能源技术可再生能源技术
  • 在工程领域的人工智能.
  • 电气工程 电气工程

背景情况:

  • 光伏 (PV) 电池板是重要的绿色能源,需要准确的参数估计来监测性能和诊断故障.
  • 现有的光伏面板参数估计方法虽然在进步,但仍有机会提高准确性和效率.

研究的目的:

  • 开发一种新的,更准确的方法来估计光伏 (PV) 面板的参数.
  • 为了提高光伏面板参数估计算法的收速度.
  • 为光伏面板健康监测和最大功率点跟踪 (MMPT) 提供改进的数据.

主要方法:

  • 利用输出电流和电压的动态反应来创建时间序列I-V向量.
  • 采用基于人工神经网络 (ANN) 的光伏模型参数范围分类器 (MPRC),在各种 I-V 数据集上进行训练.
  • 集成MPRC输出来初始化粒子群优化 (PSO) 算法用于参数估计.

主要成果:

  • 拟议的混合ANN-PSO方法在PV面板参数估计中达到3.5%的准确性.
  • 与独立的PSO方法相比,在融合速度方面取得了显著的改进.
  • 通过使用实验和生成I-V数据集的模拟验证.

结论:

  • 开发的方法有效地估计了光伏面板的参数,提高了准确性和更快的融合.
  • 这种方法为先进的光伏电池板健康监测和优化最大功率点跟踪提供了有价值的工具.
  • 整合ANN用于初始参数范围分类显著提高了PSO算法的效率.