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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

150
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...
150
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

102
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...
102
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.0K
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...
1.0K
Feedback control systems01:26

Feedback control systems

431
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...
431
Neural Regulation01:37

Neural Regulation

40.2K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
40.2K
Neural Control of Respiration01:18

Neural Control of Respiration

3.0K
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...
3.0K

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

Reducing M2 macrophage in lung fibrosis by controlling anti-M1 agent.

Scientific reports·2025
Same author

Optimal controlling of anti-TGF-[Formula: see text] and anti-PDGF medicines for preventing pulmonary fibrosis.

Scientific reports·2023
Same author

Optimal control of TGF-β to prevent formation of pulmonary fibrosis.

PloS one·2022
Same author

Optimal control and differential game solutions for social distancing in response to epidemics of infectious diseases on networks.

Optimal control applications & methods·2020
Same author

Solving Multiextremal Problems by Using Recurrent Neural Networks.

IEEE transactions on neural networks and learning systems·2017
Same author

Heat treatment modelling using strongly continuous semigroups.

Computers in biology and medicine·2015
Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026
Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026
Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026
Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026
Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026
Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Sep 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

通过多任务人工神经网络解决非线性和复杂的最佳控制问题.

Ali Emami Kerdabadi1, Alaeddin Malek2

  • 1Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, 14115-134, Iran.

Scientific reports
|July 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的多任务学习框架,使用神经网络来解决复杂的最佳控制问题. 该方法确保了哈密尔顿的最佳性,并通过流行病学和电网模型进行了验证.

更多相关视频

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

相关实验视频

Last Updated: Sep 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

科学领域:

  • 计算数学 计算数学 计算数学
  • 控制理论 控制理论
  • 人工智能的人工智能

背景情况:

  • 在各种科学和工程领域,最佳控制问题至关重要.
  • 解决非线性和复杂的最佳控制问题仍然是一个重大挑战.
  • 现有的方法往往在高维度和复杂的动态方面扎.

研究的目的:

  • 提出一种新的多任务学习框架,用于解决非线性和复杂的最佳控制问题.
  • 开发一种基于神经网络的统一方法,整合状态,控制和附加动态.
  • 为了确保满足哈密尔顿的最佳性条件.

主要方法:

  • 神经网络框架旨在统一状态,控制和附加动态.
  • 哈密尔顿式是嵌入到神经网络结构使用Pontryagin最大原则.
  • 提出了一种代算法,用于顺序和并行神经网络学习.
  • 神经网络解决方案与最佳控制解决方案的融合已被证明.

主要成果:

  • 提出的框架成功地解决了两个非线性复杂的最佳控制问题.
  • 应用包括流行病学建模和电网稳定.
  • 数字结果和图形表示证明了该方法的有效性.
  • 通过神经网络的解决方案来满足哈密尔顿的最佳性条件.

结论:

  • 多任务学习框架为复杂的最佳控制提供了一种有效的方法.
  • 神经网络集成提供了一个强大的和融合的解决方案.
  • 该方法对各种领域的现实应用具有前景.