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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 ...
Weighting and pruning based ensemble deep random vector functional link network for tabular data classification
(
Elsevier Ltd
, 2022 , Article)
In this paper, we first integrate normalization to the Ensemble Deep Random Vector Functional Link network (edRVFL). This re-normalization step can help the network avoid divergence of the hidden features. Then, we propose ...
Jointly optimized ensemble deep random vector functional link network for semi-supervised classification
(
Elsevier Ltd
, 2022 , Article)
Randomized neural networks have become more and more attractive recently since they use closed-form solutions for parameter training instead of gradient-based approaches. Among them, the random vector functional link network ...
Takagi–Sugeno fuzzy based power system fault section diagnosis models via genetic learning adaptive GSK algorithm
(
Elsevier B.V.
, 2022 , Article)
To effectively deal with the operating uncertainties of protective relays and circuit breakers existing in the power system faults, an improved fault section diagnosis (FSD) method is proposed by using Takagi–Sugeno fuzzy ...
Collaborative Truck-Drone Routing for Contactless Parcel Delivery during the Epidemic
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Article)
The COVID-19 pandemic calls for contactless deliveries. To prevent the further spread of the disease and ensure the timely delivery of supplies, this paper investigates a collaborative truck-drone routing problem for ...
Time Series Forecasting Using Online Performance-based Ensemble Deep Random Vector Functional Link Neural Network
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Conference Paper)
Time series forecasting remains a challenging task in data science while it is of great relevance to decision-making in various industries such as transportation, finance, electricity resource management, meteorology. ...
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 ...