Search
Now showing items 1-6 of 6
Significant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning
(
Elsevier Ltd
, 2023 , Article)
The reliable control of wave energy devices highly relies on the forecasts of wave heights. However, the dynamic characteristics and significant fluctuation of waves’ historical data pose challenges to precise predictions. ...
Dynamic ensemble deep echo state network for significant wave height forecasting
(
Elsevier Ltd
, 2023 , Article)
Forecasts of the wave heights can assist in the data-driven control of wave energy systems. However, the dynamic properties and extreme fluctuations of the historical observations pose challenges to the construction of ...
Graph ensemble deep random vector functional link network for traffic forecasting
(2022 , Article)
Traffic forecasting is crucial to achieving a smart city as it facilitates public transportation management, autonomous driving, and the resource relocation of the sharing economy. Traffic forecasting belongs to the ...
A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition
(
Elsevier Inc.
, 2023 , Article)
Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary ...
EEG-based emotion recognition using random Convolutional Neural Networks
(
Elsevier Ltd
, 2022 , Article)
Emotion recognition based on electroencephalogram (EEG) signals is helpful in various fields, including medical healthcare. One possible medical application is to diagnose emotional disorders in patients. Humans tend to ...
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
(
Elsevier Ltd
, 2022 , Article)
Electric load forecasting is essential for the planning and maintenance of power systems. However, its un-stationary and non-linear properties impose significant difficulties in predicting future demand. This paper proposes ...