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相关概念视频

Observational Learning01:12

Observational Learning

212
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...
212
Associative Learning01:27

Associative Learning

447
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.
Classical conditioning, also known...
447
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

450
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...
450
Purposive Learning01:22

Purposive Learning

143
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
143
Introduction to Learning01:18

Introduction to Learning

474
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
474
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

2.9K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
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相关实验视频

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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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增强基于投影的代学习控制:一个集成成员的方法.

Li Li1, Hongyang Zhao1, Fazhi Song1

  • 1Center of Ultra-precision Optoelectronic Instrument Engineering, Harbin Institute of Technology, Harbin 150080, China; Key Lab of Ultra-precision Intelligent Instrumentation (Harbin Institute of Technology), Ministry of Industry and Information Technology, Harbin 150080, China.

ISA transactions
|July 20, 2023
PubMed
概括
此摘要是机器生成的。

基于投影的代学习控制 (P-ILC) 提高了晶圆阶段的性能. 集成集成频域 ILC (SM-F-ILC) 提高了重复性和非重复性错误补偿,实现了更快的不确定性趋同.

关键词:
进食前期控制 进食前期控制代学习是一种代式的学习.运动控制器运动控制器伺服器控制器的控制器

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科学领域:

  • 控制工程 控制工程 控制工程
  • 机械电子学是什么意思 机械电子学
  • 半导体制造业 半导体制造业

背景情况:

  • 石版机床对于半导体制造要求高精度和灵活性.
  • 基于投影的代学习控制 (P-ILC) 是晶圆阶段的一个有希望的方法.
  • 现有的P-ILC方法面临着模型不确定性和错误趋同的挑战.

研究的目的:

  • 为了提高基于投影的代学习控制 (P-ILC) 对于石版机床的性能.
  • 在P-ILC框架内改进非参数和参数前控制模式.
  • 解决与重复性错误补偿,非重复性错误积累和在模型不确定性下的收速度相关的局限性.

主要方法:

  • 整合基于集合成员的频率域代学习控制 (SM-F-ILC) 算法.
  • 在P-ILC的非参数前控制模式中应用SM-F-ILC.
  • 充分利用SM-F-ILC的优势,还可以增强参数前控制模式.
  • 通过对晶圆阶段的实验结果进行验证.

主要成果:

  • 增强的P-ILC方案在补偿重复错误方面表现得更好.
  • 观察到非重复性错误积累的有效减弱.
  • 实现了快速的融合速度,即使存在模型不确定性.
  • 实验验证证了拟议的控制策略的有效性.

结论:

  • 集成SM-F-ILC显著提高了P-ILC在石版应用中的能力.
  • 改进的P-ILC方案提供了卓越的错误补偿和更快的融合,这对于晶圆阶段精度至关重要.
  • 这种方法为实现半导体制造过程中的高运动精度和灵活性提供了强大的解决方案.