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

Control Systems01:10

Control Systems

1.9K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.9K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

430
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 of...
430
Control Systems: Applications01:25

Control Systems: Applications

1.2K
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
1.2K
Feedback control systems01:26

Feedback control systems

732
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
732
Neural Control of Respiration01:18

Neural Control of Respiration

5.0K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
5.0K
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.8K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.8K

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

Forecast of photovoltaic material recycling and assessment of supply-demand gaps considering recycling feedback and subsidies.

Waste management (New York, N.Y.)·2026
Same author

Synchronization of complex dynamical networks with saturated delayed impulsive control.

ISA transactions·2024
Same author

Ultra-Short-Term Wind Farm Power Prediction Considering Correlation of Wind Power Fluctuation.

Sensors (Basel, Switzerland)·2024
Same author

Too little or too much? Exploring the effectiveness of different policies in air pollution control from technical and non-technical pathways.

Journal of environmental management·2024
Same author

A Fixed-Time Proximal Gradient Neurodynamic Network With Time-Varying Coefficients for Composite Optimization Problems and Sparse Optimization Problems With Log-Sum Function.

IEEE transactions on neural networks and learning systems·2024
Same author

Consensus of a novel heuristic nonlinear multi-agent system in DOS attack network environment via saturation impulse control mechanism.

ISA transactions·2024

相关实验视频

Updated: Feb 15, 2026

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
08:42

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

12.6K

AW-EL-PINNs:在最佳控制问题上的欧勒-拉格朗日系统的多任务学习物理信息的神经网络.

Chuandong Li1, Runtian Zeng1

  • 1College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.

Neural networks : the official journal of the International Neural Network Society
|February 13, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了适应加权欧勒-拉格朗日定理结合物理信息的神经网络 (AW-EL-PINNs) 以实现最佳控制. 与传统方法相比,AW-EL-PINNs可以提高欧勒-拉格朗系统的解决方案精度和稳定性.

关键词:
适应性损失加权方式欧勒-拉格朗日定理 欧勒-拉格朗日定理最佳控制问题 最佳控制问题基于物理学的神经网络.两个点的边界值问题

更多相关视频

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.2K
Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze
11:15

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

Published on: February 20, 2014

13.6K

相关实验视频

Last Updated: Feb 15, 2026

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
08:42

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

12.6K
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.2K
Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze
11:15

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

Published on: February 20, 2014

13.6K

科学领域:

  • 计算数学 计算数学 计算数学
  • 机器学习 机器学习
  • 最佳控制理论 最佳控制理论

背景情况:

  • 欧勒-拉格朗日系统是经典力学和最佳控制的基础.
  • 解决这些系统通常涉及复杂的数值方法和手动参数调.
  • 现有的物理信息神经网络 (PINNs) 需要大量的努力来平衡损失的功能.

研究的目的:

  • 为了提出一个新的框架,自适应加权欧勒-拉格朗日定理结合了物理信息的神经网络 (AW-EL-PINNs),用于在最佳控制下解决欧勒-拉格朗日系统.
  • 提高解决最佳控制问题的效率和准确性.
  • 在深度学习方法中减少对损失函数权重的手动调整的需求.

主要方法:

  • 拟议的AW-EL-PINNs框架将欧勒-拉格朗日定理与深度学习架构集成在一起.
  • 最佳控制问题系统地转化为两点边界值问题 (TPBVPs).
  • 适应性损失权衡机制在训练期间动态平衡损失组件,减少手动干预.

主要成果:

  • 在五个数值示例中,AW-EL-PINNs显示了增强的解决方案准确性.
  • 在整个优化过程中,框架保持了稳定性.
  • 在精度和稳定性方面,性能优于基线方法.

结论:

  • AW-EL-PINNs提供了一种强大而准确的方法,用于在最佳控制下解决欧勒-拉格朗系统.
  • 通过减少手动调整,自适应性损失权衡机制显著改善了传统PINNs.
  • 这种框架对各种物理系统中的应用具有前景,需要精确的最佳控制解决方案.