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相关概念视频

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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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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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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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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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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相关实验视频

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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针对随机神经网络的强大的噪声感知算法及其合性质.

Yuqi Xiao1, Muideen Adegoke2, Chi-Sing Leung2

  • 1Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, HKSAR, China; State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, HKSAR, China; Shenzhen Key Laboratory of Millimeter Wave and Wideband Wireless Communications, CityU Shenzhen Research Institute, Shenzhen, 518057, China.

Neural networks : the official journal of the International Neural Network Society
|February 29, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的噪声感知随机向量功能链路网络 (NARNN) 算法,以提高随机神经网络 (RNN) 在不完美的条件下 (如重量噪声和数据异常值) 的可靠性. 与现有的强大的RNN方法相比,NARNN算法表现出卓越的性能.

关键词:
半正方形的一半.网络弹性 网络弹性对于噪音的认识异常值样本的样本.随机神经网络是一种随机的神经网络.

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相关实验视频

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 神经网络的神经网络的神经网络

背景情况:

  • 随机神经网络 (RNN),包括随机向量功能链路网络 (RVFL) 和极端学习机器 (ELM),对于单隐层前网络 (SLFNs) 是有效的.
  • RNN提供强大的近似能力,但在存在重量噪声和数据异常值时可能不可靠.
  • 现有的RNN算法需要在不完美的运行条件下进行增强,以提高其稳定性.

研究的目的:

  • 开发一个强大的RNN算法,解决重量噪声和训练数据异常值的综合影响.
  • 引入一种新的目标功能和优化方法,以提高RVFL网络性能.
  • 将拟议的算法扩展到集体深度RVFL (edRVFL) 网络和故障耐受性.

主要方法:

  • 提出了一个新的目标功能,以减轻RVFL网络中重量噪声和异常值的影响.
  • 半二次优化方法用于开发噪声感知RNN (NARNN) 算法.
  • 讨论了NARNN融合的理论验证及其扩展到整体和容错配置.

主要成果:

  • 拟议的NARNN算法有效地优化了针对杂和偏差数据而设计的目标函数.
  • 在理论上已经确立了NARNN算法的收性质.
  • 实验结果表明,NARNN的性能优于现有的最先进的强大的RNN算法.

结论:

  • 对于面临重量噪声和数据异常值的RVFL网络,NARNN算法提供了一个强大的解决方案.
  • 该NARNN框架适用于集体深度RVFL网络,并且可以扩展到故障耐受性.
  • 拟议的方法在可靠性和性能方面比目前强大的RNN方法有显著的改进.