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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

83
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....
83
Associative Learning01:27

Associative Learning

275
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...
275
Equivalent Resistance01:16

Equivalent Resistance

373
In circuit analysis, situations often arise where resistors are neither in series nor parallel configurations. To tackle such scenarios, three-terminal equivalent networks like the wye (Y) (Figure 1 (a)) or tee (T) and delta (Δ) (Figure 1 (b)) or pi (π) networks come into play. These networks offer versatile solutions and are frequently encountered in various applications, including three-phase electrical systems, electrical filters, and matching networks.
373
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

59
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,...
59
State Space Representation01:27

State Space Representation

159
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...
159
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
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...
93

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相关实验视频

Updated: May 24, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

通过非对称网络进行跨领域学习的近似.

H N Mhaskar1

  • 1Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, United States of America.

Neural networks : the official journal of the International Neural Network Society
|March 6, 2025
PubMed
概括

本研究介绍了一种分析基于非对称内核的基于内核的网络的一般方法,从而推进机器学习近似能力. 它为使用ReLU网络的函数近似提供精度估计,即使非整数平滑性.

科学领域:

  • 机器学习 机器学习
  • 接近理论的近似理论.
  • 神经网络的神经网络的神经网络

背景情况:

  • 机器学习研究已经广泛研究了各种模型的近似能力 (表达力),包括神经网络和基于内核的方法,超过三十年.
  • 现有的研究往往依赖于对称或正确的确定的核心,限制了某些应用的分析范围.

研究的目的:

  • 开发一个一般框架来研究使用非对称内核的基于内核的网络的近似能力.
  • 将分析扩展到传统的正确确的内核之外,结合通用翻译网络和旋转的区域函数内核.
  • 使用 ReLU 网络获得对 Sobolev 类中函数的近似精度估计,特别是当光滑参数"r"不是整数时.

主要方法:

  • 引入了分析基于内核的网络的通用方法,超越了对非对称内核的单数值分解.
  • 被认为是一个内核家族,包括通用翻译网络和旋转区域函数内核.
  • 用非整数平滑度参数的 ReLU 网络对 Sobolev 类中的函数推导出统一近似精度估计.

主要成果:

  • 建立了一种分析基于内核的网络与非对称内核的近似能力的一般方法.
  • 获得了ReLU网络对索波列夫类函数的均近似的特定精度估计,即使对于非整数平滑度.
  • 证明了一般结果对相对于输入空间维度的低平滑度的函数的适用性.
关键词:
跨领域的学习.接近度的程度.基于神经和内核的近似计算.

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结论:

  • 提出的通用方法有效地分析了基于非对称内核的基于内核的网络,扩大了对其近似功率的理论理解.
  • 这些发现为 ReLU 网络对具有不同流性质的函数的近似精度提供了宝贵的见解.
  • 这项工作为机器学习的理论基础做出了贡献,对不变学习,转移学习和先进的成像技术有潜在的影响.