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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

159
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
159
Reinforcement Schedules01:24

Reinforcement Schedules

242
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,...
242
Observational Learning01:12

Observational Learning

314
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
314
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
Types Of Transformers01:16

Types Of Transformers

1.1K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.1K
Reinforcement01:23

Reinforcement

343
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:
343

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相关实验视频

Updated: Sep 13, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

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强化学习与时间和变量依赖意识的变压器用于股票交易优化.

Yifan Li1, Xu Dong1, Zhuang Wu1

  • 1School of Management and Engineering, Capital University of Economics and Business, Beijing, China.

Neural networks : the official journal of the International Neural Network Society
|July 30, 2025
PubMed
概括

本研究介绍了一种新的基于变压器的强化学习 (RL) 模型,用于股票交易优化. 该模型有效地捕捉了时间和可变的依赖性,显著改善了投资组合的业绩和回报.

关键词:
决策方式 决策方式强化学习是一种强化学习.股票交易优化 股票交易优化变压器变压器变压器

相关实验视频

Last Updated: Sep 13, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

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科学领域:

  • 量化金融 量化金融
  • 机器学习 机器学习
  • 金融工程是金融工程.

背景情况:

  • 股票交易优化对于动态市场的金融决策至关重要.
  • 现有的变压器强化学习 (RL) 方法往往忽视多变量相互作用,限制了政策学习.
  • 需要模型来捕捉市场数据中的时间和交叉变量依赖关系.

研究的目的:

  • 提出一个新的RL模型,集成一个时间和可变依赖感知变压器,以优化股票交易.
  • 通过捕捉不同的依赖关系来增强市场数据的表现.
  • 改善可用于RL政策学习的决策信息.

主要方法:

  • 开发了一个时间和可变依赖意识的变压器,集成到强化学习 (RL) 框架中.
  • 实施了短期和长期预测模块,利用变压器进行依赖性探索.
  • 整合了股票资产相关性关系表示模块和用于融合表示的政策决策模块.

主要成果:

  • 拟议的RL模型实现了1.48的夏普比率和2.65的投资组合回报率.
  • 与CSI-300,S&P-100和NASDAQ-100数据集上的最先进的方法相比,表现出卓越的性能.
  • 该模型有效地捕获了市场数据中的复杂的时间和可变依赖性.

结论:

  • 时间和可变依赖感知变压器在RL框架内显著提高了股票交易优化.
  • 模型学习多种依赖关系的能力导致了更全面的决策信息.
  • 这种方法为改进自动化交易策略和投资组合管理提供了一个有希望的方向.