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相关概念视频

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Reinforcement Schedules

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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.
Once a behavior is learned,...
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Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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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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Updated: Jun 28, 2025

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一种基于新方法的强化学习,具有深度时差网络,用于灵活的双店调度问题.

Xiao Wang1, Peisi Zhong2, Mei Liu3

  • 1College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.

Scientific reports
|April 19, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度时间差强化学习算法有效地解决了灵活的双工厂调度问题 (FDSSP),最大限度地减少了生产时间. 这种方法通过优化复杂的生产环境中的任务安排来提高制造效率.

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科学领域:

  • 运营研究 运营研究
  • 人工智能的人工智能
  • 制造业 工程 制造工程

背景情况:

  • 灵活的双工厂调度问题 (FDSSP) 整合了工厂和装配厂的复杂性.
  • 在制造业中,相关任务之间的调度关联带来了重大挑战.

研究的目的:

  • 开发一种有效的算法,以尽量减少FDSSP中的 makespan.
  • 在灵活的制造系统中解决复杂的任务安排协会.

主要方法:

  • 制定了FDSSP作为一个包含组装约束的数学模型.
  • 将问题转化为直接战略选择的马尔科夫决策过程.
  • 利用一个深度神经网络,有十个状态特征和八个用于决策的构建式启发式.
  • 实施了深度时间差强化学习框架.

主要成果:

  • 与现有方法相比,拟议的算法表现出优越的性能.
  • 有效地减少了对灵活的双店日程安排问题的制造量.
  • 通过广泛的比较实验来验证.

结论:

  • 开发的深度时间差强化学习算法为FDSSP提供了强大的解决方案.
  • 这种方法对提高制造业效率具有实际意义.
  • 在灵活制造中成功地解决了复杂的调度协会.