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相关概念视频

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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...
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Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
207
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
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Modeling the Functional Network for Spatial Navigation in the Human Brain
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为受限制的博尔兹曼机器通过网络梯度优化连接.

Amanda Camacho Novaes de Oliveira1, Daniel Ratton Figueiredo1

  • 1Systems Engineering and Computer Science (PESC), Federal University of Rio de Janeiro (UFRJ), Av. Athos da Silveira Ramos 149, Bloco H-319, Cidade Universitária, 21945-970, Rio de Janeiro, RJ, Brazil.

Neural networks : the official journal of the International Neural Network Society
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PubMed
概括

网络连接梯度 (NCG) 在受限制的博尔兹曼机器 (RBM) 中优化稀疏连接. 这种方法共同学习网络参数和连接性,以提高样本生成和分类等任务的性能.

关键词:
在AutoML中使用AutoML.网络优化 网络优化网络修剪是为了修剪网络.神经网络的神经网络的神经网络有限制的博尔茨曼机器 (RBM)

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科学领域:

  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 人工智能的人工智能

背景情况:

  • 稀疏的网络连接可以增强深层神经网络.
  • 网络连接对于浅层网络学习至关重要,比如受限制的博尔兹曼机器 (RBM).
  • 在RBM中优化稀疏连接是具有挑战性的,通常依赖于超参数.

研究的目的:

  • 引入网络连接梯度 (NCG),这是RBM的新型优化方法.
  • 为了使RBM参数和网络连接的共同学习,而不会改变基于能源的目标.
  • 有效地发现最佳的稀疏连接模式,以提高RBM性能.

主要方法:

  • NCG利用网络梯度来确定每个连接的影响.
  • 一个连续连接强度参数是由梯度驱动的,以定义连接模式.
  • RBM参数和网络连接是共同学习的,具有不同的学习率.

主要成果:

  • 将NCG应用于MNIST和其他数据集,从而获得了改进的RBM模型.
  • 该方法在样本生成和分类的基准任务中表现出卓越的性能.
  • NCG证明对网络初始化具有强大性能,并且能够动态添加/删除连接.

结论:

  • NCG提供了一种有效的方法来优化RBM中的稀疏连接.
  • 该方法在生成和区分任务中实现了卓越的性能.
  • 在RBM中,NCG提供了一种原则和灵活的方式来学习RBM中的网络结构.