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相关概念视频

Multi-input and Multi-variable systems01:22

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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.
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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.
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
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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.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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相关实验视频

Updated: May 10, 2025

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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多代理深度强化学习用于传感器启用零售供应链的综合需求预测和库存优化.

Yongbin Yang1, Mengdie Wang2, Jiyuan Wang3

  • 1Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
概括

本研究介绍了零售供应链的新深度强化学习框架,改进了需求预测和库存管理. 该模型使用实时传感器数据将预测错误减少18.2%,库存减少23.5%.

关键词:
需求预测需要预测.在库存优化,库存优化.多种代理强化学习的多种代理强化学习供应链管理 供应链管理

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科学领域:

  • 供应链管理 供应链管理
  • 人工智能的人工智能
  • 运营研究 运营研究

背景情况:

  • 由于消费者的动态行为和市场波动,零售供应链在需求预测和库存管理方面面临重大挑战.
  • 现有的统计和机器学习模型难以同时捕捉复杂的时间依赖性和优化库存决策.
  • 物联网 (IoT) 传感器,RFID和智能货架的整合为增强供应链可见性提供了新的数据流.

研究的目的:

  • 提出一个新的多代理深度强化学习 (DRL) 框架,用于零售业的综合需求预测和库存管理.
  • 利用各种数据源,包括历史销售和实时传感器数据,以提高运营效率.
  • 解决传统方法在处理复杂的时间模式,促销效应和环境因素方面的局限性.

主要方法:

  • 一种混合方法,将基于变压器的序列建模用于需求预测,与用于库存控制的等级强化学习代理相结合.
  • 利用注意力机制来处理历史销售数据和实时传感器测量 (温度,湿度) 以识别模式.
  • 通过多代理强化学习在整个分销网络中协调库存决策.

主要成果:

  • 与最先进的基线相比,需求预测错误减少了18.2%.
  • 通过优化库存管理,减少了23.5%的缺货率.
  • 在促销活动和季节性过渡期间,在预测和库存控制方面取得了显著的改进.

结论:

  • 拟议的多代理DRL框架提供了一个可扩展和有效的解决方案,用于优化传感器支持的零售供应链中的综合需求预测和库存管理.
  • 利用实时传感器数据和先进的人工智能技术在动态的零售环境中提供了竞争优势.
  • 这项研究推进了DLR在优化复杂的,现实世界的供应链运作中的应用.