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相关概念视频

Maximum Power Transfer01:16

Maximum Power Transfer

416
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
416
Reinforcement Schedules01:24

Reinforcement Schedules

243
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
243
Reinforcement01:23

Reinforcement

347
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
347
Control of Power Flow01:30

Control of Power Flow

317
There are several methods to control power flow in power systems:
317
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

184
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
184
Load-frequency control01:28

Load-frequency control

263
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
263

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相关实验视频

Updated: Sep 16, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

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Published on: February 14, 2025

621

分布式多代理深度增强基于学习的传输功率控制在蜂网络.

Hun Kim1, Jaewoo So1

  • 1Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多代理深度强化学习 (MADRL) 方法,用于蜂网络传输功率控制的长期短期记忆 (LSTM). 该方法通过使基站能够使用本地数据有效地管理干扰来提高总比率的性能.

关键词:
集中培训与分散执行的集中培训.多代理的深度强化学习学习.多细胞网络的多细胞网络.传输功率控制器 传输功率控制器

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相关实验视频

Last Updated: Sep 16, 2025

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科学领域:

  • 无线通信无线通信
  • 人工智能的人工智能
  • 网络工程 网络工程

背景情况:

  • 干扰管理对于多细胞网络的性能至关重要.
  • 控制基站下链传输功率对于高数据速率至关重要,但随着细胞密度的增加而变得复杂.

研究的目的:

  • 提出使用多代理深度增强学习 (MADRL) 的传输功率控制方案,以最大限度地提高多细胞网络中的总比率.
  • 通过整合长期短期记忆 (LSTM) 架构来增强MADRL,以改善状态保留和决策.

主要方法:

  • 开发了一种多代理深度增强学习 (MADRL) 框架,用于下链传输功率控制.
  • 长期短期内存 (LSTM) 架构被纳入MADRL代理,以利用历史状态信息.
  • 每个基站代理仅使用本地通道状态信息,最大限度地减少代理之间的通信.

主要成果:

  • 拟议的MADRL-LSTM方案显示,与现有的MADRL方法相比,总比率性能得到了改进.
  • 该方案有效地减少了基站链路之间交换的信号信息数量.
  • 当地通道状态信息的利用对于去中心化控制来说是有效的.

结论:

  • 在密集的蜂网络中,LSTM与MADRL的集成为传输功率控制提供了强大的解决方案.
  • 使用本地信息进行分散控制是可行的和高效的,减少了通讯开销.
  • 拟议的方法通过有效的干扰管理来提高整体网络容量和用户数据速率.