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A systematic review of data-driven approaches to fault diagnosis and early warning
As an important stage of life cycle management, machinery PHM (prognostics and health
management), an emerging subject in mechanical engineering, has seen a huge amount of …
management), an emerging subject in mechanical engineering, has seen a huge amount of …
[HTML][HTML] Electricity price forecasting: A review of the state-of-the-art with a look into the future
A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the
last 15 years, with varying degrees of success. This review article aims to explain the …
last 15 years, with varying degrees of success. This review article aims to explain the …
Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL),
have created tremendous excitement and opportunities in the earth and environmental …
have created tremendous excitement and opportunities in the earth and environmental …
Learning long-term dependencies with gradient descent is difficult
Recurrent neural networks can be used to map input sequences to output sequences, such
as for recognition, production or prediction problems. However, practical difficulties have …
as for recognition, production or prediction problems. However, practical difficulties have …
Deep learning: Evolution and expansion
This paper historically attempts to map the significant success of deep neural networks in
notably varied classification problems and application domains with near human-level …
notably varied classification problems and application domains with near human-level …
Learning long-term dependencies in NARX recurrent neural networks
It has previously been shown that gradient-descent learning algorithms for recurrent neural
networks can perform poorly on tasks that involve long-term dependencies, ie those …
networks can perform poorly on tasks that involve long-term dependencies, ie those …
Computational capabilities of recurrent NARX neural networks
Recently, fully connected recurrent neural networks have been proven to be computationally
rich-at least as powerful as Turing machines. This work focuses on another network which is …
rich-at least as powerful as Turing machines. This work focuses on another network which is …
The problem of learning long-term dependencies in recurrent networks
The authors seek to train recurrent neural networks in order to map input sequences to
output sequences, for applications in sequence recognition or production. Results are …
output sequences, for applications in sequence recognition or production. Results are …
Long-term wind speed and power forecasting using local recurrent neural network models
This paper deals with the problem of long-term wind speed and power forecasting based on
meteorological information. Hourly forecasts up to 72-h ahead are produced for a wind park …
meteorological information. Hourly forecasts up to 72-h ahead are produced for a wind park …
Input-output HMMs for sequence processing
We consider problems of sequence processing and propose a solution based on a discrete-
state model in order to represent past context. We introduce a recurrent connectionist …
state model in order to represent past context. We introduce a recurrent connectionist …