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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.1K
Dynamic Equilibrium02:20

Dynamic Equilibrium

61.8K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
61.8K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

292
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...
292
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

1.2K
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
1.2K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

394
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...
394

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

Recent Advances on Off-Policy Reinforcement Learning for Optimization Control.

IEEE transactions on cybernetics·2026
Same author

Event-triggered decentralized adaptive critic learning control for interconnected systems with nonlinear inequality state constraints.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Cross-linked genes analysis of programmed cell death and network pharmacological validation after spinal cord injury.

Biochemical and biophysical research communications·2025
Same author

Design Strategies and Advanced Methods for Cathode Engineering in Aqueous Zinc-Iodine Batteries.

Small methods·2025
Same author

FX-DARTS: Designing Topology-Unconstrained Architectures With Differentiable Architecture Search and Entropy-BasedSuper-Network Shrinking.

IEEE transactions on neural networks and learning systems·2025
Same author

Parallel Multistep Evaluation With Efficient Data Utilization for Safe Neural Critic Control and Its Application to Orbital Maneuver Systems.

IEEE transactions on neural networks and learning systems·2025

相关实验视频

Updated: Jan 17, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.8K

联合学习适应性动态编程大规模的多代理人中场游戏基于最佳共识的最佳共识.

Mingduo Lin, Guoling Yuan, Derong Liu

    IEEE transactions on cybernetics
    |September 22, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了大型多代理系统的联合学习自适应动态编程 (FL-ADP) 控制方案. 这种新的方法通过使用平均场游戏 (MFGs) 接近代理相互作用来确保稳定的最佳共识控制.

    更多相关视频

    The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
    06:18

    The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

    Published on: October 20, 2022

    2.5K

    相关实验视频

    Last Updated: Jan 17, 2026

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.8K
    The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
    06:18

    The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

    Published on: October 20, 2022

    2.5K

    科学领域:

    • 控制理论 控制理论
    • 人工智能的人工智能
    • 分布式系统 分布式系统

    背景情况:

    • 由于众多的相互作用和冲突,大规模的多代理系统面临着实现实时最佳共识控制的挑战.
    • 现有的方法难以应对这些系统的复杂性和规模,需要先进的控制策略.

    研究的目的:

    • 开发一种新的联合学习自适应动态编程 (FL-ADP) 控制方案,以在大型多代理系统中实现最佳共识.
    • 为应对大规模代理人互动和利益冲突所带来的挑战.
    • 解决基于平均场游戏 (MFGs) 的最佳共识问题.

    主要方法:

    • 使用平均场游戏 (MFGs) 估计单个药剂相互作用.
    • 开发一种新的非折扣性绩效指数函数,包括平均场合和跟踪错误.
    • 利用一个批评质量神经网络来解决合的汉密尔顿 - 雅各比 - 贝尔曼和福克 - 普朗克 - 科尔摩戈罗夫方程.
    • 制定一个由事件触发的联合学习机制,以实现算法融合和通信效率.

    主要成果:

    • 推导一个近似的最佳控制政策和量化集体行为概率密度.
    • 使用莱普诺夫直接方法,保证追踪错误和重量估计错误的统一终极边界性.
    • 验证FL-ADP计划的有效性和合理性,通过对大型多重无人机空中飞行器系统的模拟.

    结论:

    • 拟议的FL-ADP控制方案有效地解决了大规模多代理系统中的最佳共识问题.
    • 该方法平衡了通信资源消耗与算法融合,优于现有方法.
    • 开发的技术为大规模系统的实时自适应性最佳共识控制提供了强大而高效的解决方案.