坚固的故障感知极端学习机器,基于最大的电流
概括
本研究介绍了一种强大的故障感知极端学习机器 (ELM) 算法,以改善噪音和故障引起的性能下降. 新的算法在各种数据集和条件中展示了卓越的稳定性和概括性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 神经网络的神经网络的神经网络
背景情况:
- 极端学习机器 (ELM) 是通用近似的强大工具.
- 实际的ELM性能经常受到重量噪声,节点故障和异常值的影响.
- 现有的ELM算法在现实世界,不完美的条件下缺乏足够的稳定性.
研究的目的:
- 开发一个强大的极端学习机器 (ELM) 算法,能够抵御噪音和故障.
- 在不利条件下增强网络的稳定性和通用化能力.
- 引入一个新的目标函数,集成异常值阻力,以提高网络性能.
主要方法:
- 考虑重量噪声和节点故障的经典ELM平方误差的分析.
- 对异常电阻的最大电流标准 (MCC) 的整合.
- 开发和融合证明强大的故障识别ELM (RFAELM) 算法.
主要成果:
- 拟议的RFAELM算法在各种噪音和故障级别方面表现出显著的稳定性.
- 对八个基准数据集的评估证实了算法的优越泛化能力.
- 统计比较显示,RFAELM的表现优于现有的强大的ELM算法.
结论:
- 强大的故障感知ELM (RFAELM) 有效地解决了ELM的性能下降.
- RFAELM提供了增强的网络弹性和通用性,证明了其实际适用性.
- 这种新的算法比目前强大的ELM技术有了显著的进步.
相关概念视频
Propagation of Uncertainty from Random Error
1.1K
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...
1.1K
Introduction to Learning
533
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
533
Associative Learning
579
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...
Classical conditioning, also known...
579
Machines: Problem Solving II
373
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
373
Routh-Hurwitz Criterion II
411
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
411
Residuals and Least-Squares Property
7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K

