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相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
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An RC circuit consists of resistance and capacitance, while in an RL circuit, capacitance is replaced by an inductor. RL and RC circuits are first-order differential circuits that store energy. An RC circuit stores energy in the electric field, while an RL circuit stores energy in the magnetic field. When connected to a battery, an RC circuit charges the capacitor, causing the current to decrease from maximum to zero upon being fully charged. This increases the voltage across the capacitor from...
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Second Derivatives and Laplace Operator01:22

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The first order operators using the del operator include the gradient, divergence and curl. Certain combinations of first order operators on a scalar or vector function yield second order expressions. Second-order expressions play a very important role in mathematics and physics. Some second order expressions include the divergence and curl of a gradient function, the divergence and curl of a curl function, and the gradient of a divergence function.
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Updated: Jul 19, 2025

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使用深度 ReLU 网络对平滑函数的近似计算.

Linhao Song1, Ying Liu2, Jun Fan3

  • 1School of Mathematical Science, Beihang University, Beijing, China; School of Data Science, City University of Hong Kong, Kowloon, Hong Kong.

Neural networks : the official journal of the International Neural Network Society
|August 7, 2023
PubMed
概括
此摘要是机器生成的。

深度神经网络,特别是功能深度ReLU网络,对非线性连续函数的近似能力有所提高. 这项研究为ReLU网络提供了新的理论分析,为各种功能空间提供了更好的近似率.

关键词:
接近理论的近似理论.深度学习理论理论 深度学习理论弗雷切特衍生品的衍生品多项式的比率是多项式的比率.在 ReLULU 中,你会看到 ReLULU.顺的函数的函数.

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科学领域:

  • 应用数学 应用数学 应用数学
  • 机器学习理论机器学习理论
  • 数字分析 数字分析

背景情况:

  • 深度神经网络用于近似非线性连续函数.
  • 现有的理论分析存在局限性,特别是在修正线性单位 (ReLU) 激活函数方面.
  • 需要对功能深度ReLU网络的近似功率有更好的理论理解.

研究的目的:

  • 为了研究功能深度ReLU网络的近似功率.
  • 分析具有受限连续性模块的连续函数和具有更高阶Fréchet导数的连续函数的近似能力.
  • 开发新的网络结构,用于功能近似的特征提取.

主要方法:

  • 提出一种新型的功能网络架构,设计用于更高阶的流性特征提取.
  • 根据网络深度,宽度和重量的总数,推导出数量近似率.
  • 在霍尔德和分析函数空间上分析近似误差.

主要成果:

  • 在霍尔德空间的单位球上实现了对数近似率.
  • 建立了分析函数空间的近多项式近似率.
  • 与现有文献相比,证明了更好的近似结果,特别是对于ReLU网络.

结论:

  • 功能深度ReLU网络对于非线性连续函数具有显著的近似能力.
  • 拟议的网络结构和理论分析为理解功能近似的深度学习提供了坚实的基础.
  • 这项工作推进了对深层ReLU网络在近似复杂数学函数方面的理论理解.