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相关概念视频

State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
519
State Space to Transfer Function01:21

State Space to Transfer Function

552
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
552
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Transfer Function to State Space01:23

Transfer Function to State Space

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
748
Associative Learning01:27

Associative Learning

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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.
Classical conditioning, also known...
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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.
In the absence of...
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相关实验视频

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超越经验的学习:一般化到看不见的状态空间,使用储库计算.

Declan A Norton1,2, Yuanzhao Zhang3, Michelle Girvan1,2,3,4

  • 1Department of Physics, University of Maryland, College Park, Maryland 20742, USA.

Chaos (Woodbury, N.Y.)
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概括
此摘要是机器生成的。

储水库计算是一种机器学习方法,可以在没有先前结构知识的情况下对新的动态系统行为进行概括. 一种新的训练方法使得对未观察到的系统状态进行概括,即使是从有限的数据.

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科学领域:

  • 动态系统建模动态系统建模
  • 机器学习是机器学习.
  • 复杂系统分析 复杂系统分析

背景情况:

  • 机器学习模型通常无法在没有明确的结构假设的情况下超越训练数据进行概括.
  • 储计算是一种机器学习框架,用于动态系统的数据驱动建模.

研究的目的:

  • 为了证明储库计算能够在没有结构先验的情况下对未被探索的动态进行概括.
  • 为加强水库计算机培训引入多个轨迹的培训计划.

主要方法:

  • 为水库计算机开发了多个轨迹的训练方案.
  • 从动态系统中训练有素的储计算机对离散时间序列数据进行训练.
  • 将受过训练的模型应用于具有多个吸引力盆地的多稳定系统.

主要成果:

  • 储计算机演示了对状态空间未被观察到的区域的概括.
  • 多路径培训方案提高了可用的培训数据的有效使用.
  • 在一个吸引力盆地的数据上训练的模型捕获了未观察到的盆地的行为.

结论:

  • 储计算可以在动态系统建模中实现域外概括.
  • 拟议的培训计划提高了水库计算的稳定性和适用性.
  • 这种方法推进了对具有有限观测数据的复杂系统的数据驱动建模.