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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

79
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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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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PD Controller: Design01:26

PD Controller: Design

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Deep Neural Networks for Image-Based Dietary Assessment
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单层隐藏神经网络,随机权重基于非可区分的函数.

Yoleidy Huerfano-Maldonado, Karina Vilches-Ponce, Marco Mora

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    此摘要是机器生成的。

    本研究引入了一个新的框架,使用随机权重神经网络中的非可区分函数,如随机向量功能链路 (RVFL) 网络和极端学习机器 (ELM). 新方法显著减少了计算时间,并保持了各种数据集的高精度.

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    科学领域:

    • 机器学习 机器学习
    • 计算科学 计算科学
    • 人工智能的人工智能

    背景情况:

    • 在机器学习中,不可区分的函数至关重要.
    • 随机权重神经网络,包括RVFL和ELM,受益于高效的客观功能.

    研究的目的:

    • 开发一个新的框架,将不可区分的函数集成到RVFL和ELM目标函数中.
    • 提高计算效率并保持机器学习模型的准确性.

    主要方法:

    • 在RVFL和ELM目标函数中包含了六个不可区分的函数 (规范 $L_{1,1}$, $L_{1,2}$, $L_{2,2}$,AbsMin,AbsMax,MaxMin).
    • 利用福里埃随机赋值作为激活函数,以提高稳定性.
    • 在12个基准数据集上对$L_{2,1}$-RF-ELM进行了算法评估.

    主要成果:

    • 使用非可区分函数的算法在各种数据集大小中实现了高精度.
    • 在训练和测试阶段显著减少了计算时间.
    • 在效率方面表现优于基于$L_{2,1}$的算法和标准机器学习方法.

    结论:

    • 拟议的框架有效地将不可区分的功能集成到随机权重神经网络中.
    • 这种方法提供了一个计算效率高的替代方案,而不会牺牲预测性能.
    • 这些发现表明了优化机器学习算法的有希望的方向.