A review on extreme learning machine
Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward
neural network (SLFN), which converges much faster than traditional methods and yields …
neural network (SLFN), which converges much faster than traditional methods and yields …
Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting
In this paper, an efficient new hybrid time series forecasting model combining variational
mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder …
mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder …
[HTML][HTML] Smart home energy management systems: Research challenges and survey
Electricity is establishing ground as a means of energy, and its proportion will continue to
rise in the next generations. Home energy usage is expected to increase by more than 40 …
rise in the next generations. Home energy usage is expected to increase by more than 40 …
[PDF][PDF] 极限学习机前沿进展与趋势
徐睿， 梁循， 齐金山， 李志宇， 张树森 - 计算机学报, 2019 - cjc.ict.ac.cn
摘要极限学习机(ExtremeLearningMachine, ELM) 作为前馈神经网络学习中一种全新的训练
框架, 在行为识别, 情感识别和故障诊断等方面被广泛应用, 引起了各个领域的高度关注和深入 …
框架, 在行为识别, 情感识别和故障诊断等方面被广泛应用, 引起了各个领域的高度关注和深入 …
Regularized robust broad learning system for uncertain data modeling
Abstract Broad Learning System (BLS) has achieved outstanding performance in
classification and regression problems. Specifically, the accuracy and efficiency can be …
classification and regression problems. Specifically, the accuracy and efficiency can be …
Ultra-short-term wind power prediction by salp swarm algorithm-based optimizing extreme learning machine
L Tan, J Han, H Zhang - IEEE Access, 2020 - ieeexplore.ieee.org
Wind power generation accounts for an increasing proportion of the power grid, so efficient
and accurate real-time wind power prediction is particularly important for wind power grid. In …
and accurate real-time wind power prediction is particularly important for wind power grid. In …
Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm
YD Zhang, G Zhao, J Sun, X Wu, ZH Wang… - Multimedia Tools and …, 2018 - Springer
Pathological brain detection is an automated computer-aided diagnosis for brain images.
This study provides a novel method to achieve this goal. We first used synthetic minority …
This study provides a novel method to achieve this goal. We first used synthetic minority …
Robustified extreme learning machine regression with applications in outlier-blended wind-speed forecasting
Wind energy is a core sustainable source of electric power, and accurate wind-speed
forecasting is pivotal to enhancing the power stability, efficiency, and utilization. The existing …
forecasting is pivotal to enhancing the power stability, efficiency, and utilization. The existing …
Enhanced hierarchical symbolic dynamic entropy and maximum mean and covariance discrepancy-based transfer joint matching with Welsh loss for intelligent cross …
C Yang, M Jia, Z Li, M Gabbouj - Mechanical Systems and Signal …, 2022 - Elsevier
Abstract Domain adaptation (DA) as a critical and valuable tool is devoted to minimizing the
distribution discrepancy across domains, which has been successfully utilized in intelligent …
distribution discrepancy across domains, which has been successfully utilized in intelligent …
Random vector functional link with ε-insensitive Huber loss function for biomedical data classification
BB Hazarika, D Gupta - Computer methods and programs in biomedicine, 2022 - Elsevier
Background and objective Biomedical data classification has been a trending topic among
researchers during the last decade. Biomedical datasets may contain several features …
researchers during the last decade. Biomedical datasets may contain several features …