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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

481
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
481
Linear time-invariant Systems01:23

Linear time-invariant Systems

859
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
859
Purposive Learning01:22

Purposive Learning

430
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...
430
State Space Representation01:27

State Space Representation

515
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...
515
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

383
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...
383
Observational Learning01:12

Observational Learning

807
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...
807

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相关实验视频

Updated: Jan 12, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K

从广义静止随机过程中学习网络.

Anirudh Rayas1, Jiajun Cheng1, Rajasekhar Anguluri2

  • 1School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA.

IEEE transactions on signal and information processing over networks
|November 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的方法,用于绘制复杂系统中使用节点数据的网络连接. 该方法可以准确地识别网络结构,即使是在大型,高维的场景中.

关键词:
保护法是关于保护的.网络拓推断推断的网络拓学.频谱精度矩阵是一个精确的矩阵.l1 - 规范化的惠特尔的概率估计器.

相关实验视频

Last Updated: Jan 12, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K

科学领域:

  • 网络科学 网络科学
  • 统计推理 统计推理
  • 系统工程 系统工程

背景情况:

  • 具有潜在输入的复杂网络系统在神经科学,金融和工程领域普遍存在.
  • 一个关键的挑战是从观察到的节点潜力推断网络边缘连接.
  • 经常遇到由稳定状态线性保存定律支配的系统.

研究的目的:

  • 从节点潜力开发一种学习边缘连接在复杂的网络系统的方法.
  • 解决网络推理在高维设置中的挑战,网络大小超过样本大小.
  • 为学习网络结构的准确性提供理论保证.

主要方法:

  • 使用一个 $\ell_1$-规范化的 Whittle 的最大概率估计器 (MLE) 在时间相关节点潜在样本上.
  • 假设隐性输入遵循一个具有已知的光谱密度矩阵的广义静止随机过程.
  • 利用拉普拉斯矩阵的稀疏性模式来编码网络结构.

主要成果:

  • MLE问题被证明是严格凸起的,确保了唯一的解决方案.
  • 在新的相互不一致条件和特定的样本大小约束下,ML估计准确地恢复了网络的稀疏性模式.
  • 对于拉普拉斯矩阵,在元素明智的最大值,弗罗贝尼乌斯和操作员规范中提供了回收保证.

结论:

  • 提出的 $\ell_1$ 规范化的 MLE 方法有效地推断了复杂系统中的网络连接性.
  • 该方法在高维设置中表现出强大的性能,并提供了强大的理论保证.
  • 该方法通过对工程和真实世界的神经网络数据集的模拟来验证.