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

41
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...
41

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

Integrative transcriptomic and systems biology analysis of public SLE datasets identifies immune regulatory pathways.

Journal of computer-aided molecular design·2026
Same author

Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.

Probiotics and antimicrobial proteins·2026
Same author

Designing of sorafenib analogs to target c-Raf for the management of hepatocellular carcinoma: Molecular dynamics and mmPBSA analysis.

Journal of bioinformatics and computational biology·2026
Same author

Retraction Note: Plant disease recognition using residual convolutional enlightened Swin transformer networks.

Scientific reports·2026
Same author

An improved crayfish optimization algorithm for solving engineering optimization problems.

PloS one·2026
Same author

Multi-strategy remora optimization algorithm for color multi-threshold image segmentation.

PloS one·2026

相关实验视频

Updated: Jun 6, 2025

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

6.1K

一个基于人工蜂鸟算法的混沌水平垂直交叉,用于精确的PEMFC参数估计.

Pradeep Jangir1,2,3,4,5, Absalom E Ezugwu6, Kashif Saleem7

  • 1University Centre for Research and Development, Chandigarh University, Gharuan, Mohali, 140413, India.

Scientific reports
|November 28, 2024
PubMed
概括
此摘要是机器生成的。

一个新的算法,莱维混沌人造蜂鸟算法 (LCAHA),准确地识别了质子交换膜燃料电池 (PEMFC) 模型中的参数. 在精度和速度方面,LCAHA的性能优于传统方法,确保可靠的燃料电池性能.

关键词:
人工蜂鸟算法的人工蜂鸟算法电气工程优化 电气工程优化这就是LCAHAHA的意思.最佳参数估计的最佳参数估计.在PEM燃料电池中,燃料电池是最重要的.

更多相关视频

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.0K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

相关实验视频

Last Updated: Jun 6, 2025

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

6.1K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.0K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

科学领域:

  • 工程 工程师 工程师 工程师
  • 计算智能是一种计算智能.
  • 可再生能源可再生能源是可再生能源.

背景情况:

  • 质子交换膜燃料电池 (PEMFCs) 对清洁能源至关重要,但准确的参数识别具有挑战性.
  • 现有的优化算法经常与PEMFC模型的复杂性和非线性作斗争.

研究的目的:

  • 为准确的PEMFC参数识别开发一个增强的优化算法.
  • 引入莱维混沌人工蜂鸟算法 (LCAHA) 以改进PEMFC建模.

主要方法:

  • 提出了莱维混沌人工蜂鸟算法 (LCAHA),集成混沌映射,莱维飞行和一种新的食策略.
  • 使用LCAHA来识别PEMFC模型中的未知参数.
  • 与粒子优化 (PSO),差异演化 (DE),灰狼优化器 (GWO) 和搜索算法 (SSA) 进行比较.

主要成果:

  • 与PSO (0.1924) 和GWO (0.0364) 相比,LCAHA的平方错误总和 (SSE) 达到了0.0254的显着较低.
  • LCAHA表现出优越的稳定性,标准偏差为4.59E-08.
  • 与DE和SSA相比,LCAHA表现出更快的趋同,运行时间减少了约47%,与DE和SSA相比.
  • 在六个PEMFC堆中,模拟和实际的I-V曲线使用LCAHA显示出密切的对齐.

结论:

  • 对于PEMFC参数识别,LCAHA提供了卓越的准确性,稳定性和效率.
  • 拟议的算法验证了其对现实世界PEMFC应用的稳定性和可靠性.
  • 与燃料电池建模现有的优化技术相比,LCAHA是一个显著的进步.