A comprehensive review of extreme learning machine on medical imaging
The feedforward neural network based on randomization has been of great interest in the
scientific community, particularly extreme learning machines, due to its simplicity, training …
scientific community, particularly extreme learning machines, due to its simplicity, training …
A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies
The operation and scheduling management of smart grids are important aspects, and wind
speed forecasting modules are indispensable in wind power system management …
speed forecasting modules are indispensable in wind power system management …
Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine
H Pan, Z Lü, H Wang, H Wei, L Chen - Energy, 2018 - Elsevier
Battery health monitoring and management is critically important for electric vehicle
performance and economy. This paper presents a multiple health indicators-based and …
performance and economy. This paper presents a multiple health indicators-based and …
An evolutionary robust solar radiation prediction model based on WT-CEEMDAN and IASO-optimized outlier robust extreme learning machine
C Zhang, L Hua, C Ji, MS Nazir, T Peng - Applied Energy, 2022 - Elsevier
As a kind of clean energy, solar energy occupies a pivotal position in energy applications.
Accurate and reliable solar radiation prediction is critical to the application of solar energy. In …
Accurate and reliable solar radiation prediction is critical to the application of solar energy. In …
Innovative ensemble system based on mixed frequency modeling for wind speed point and interval forecasting
Wind speed forecasting can improve wind energy utilization and is thus highly significant for
wind power systems; however, it is a challenging process. Forecasting techniques from …
wind power systems; however, it is a challenging process. Forecasting techniques from …
Machine learning to estimate surface soil moisture from remote sensing data
H Adab, R Morbidelli, C Saltalippi, M Moradian… - Water, 2020 - mdpi.com
Soil moisture is an integral quantity parameter in hydrology and agriculture practices.
Satellite remote sensing has been widely applied to estimate surface soil moisture …
Satellite remote sensing has been widely applied to estimate surface soil moisture …
Carbon price forecasting system based on error correction and divide-conquer strategies
X Niu, J Wang, L Zhang - Applied Soft Computing, 2022 - Elsevier
Carbon price forecasting is an important component of a sound carbon price market
mechanism. The accurate prediction of carbon prices is an active topic of research …
mechanism. The accurate prediction of carbon prices is an active topic of research …
Extreme learning machine and adaptive sparse representation for image classification
Recent research has shown the speed advantage of extreme learning machine (ELM) and
the accuracy advantage of sparse representation classification (SRC) in the area of image …
the accuracy advantage of sparse representation classification (SRC) in the area of image …
A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections
Under the dual stimulus of the new energy demand and the increasing competitiveness of
wind energy, the construction of wind speed prediction models began to be placed in a …
wind energy, the construction of wind speed prediction models began to be placed in a …
A novel selective ensemble system for wind speed forecasting: From a new perspective of multiple predictors for subseries
Wind speed forecasting is of considerable economic and social significance; however, it
remains challenging. Most state-of-the-art methods attempt to select the optimal predictor for …
remains challenging. Most state-of-the-art methods attempt to select the optimal predictor for …