Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

To overcome the shortcomings of traditional roller bearing remaining useful life prediction
methods, which mainly focus on prediction accuracy improvement and ignore labor cost and …

Recurrent Neural Networks (RNNs) have become competitive forecasting methods, as most
notably shown in the winning method of the recent M4 competition. However, established …

Coke price prediction is critical for smart coking plants to make sensible production plan.
The prediction of coke price fluctuations is a time-series problem, and gated recurrent unit …

The failures of mechanical structure featuring high nonlinearity, non-normal and non-
independent are implicit function and small-probability events. This normally results in low …

The quality of operation of neural networks in solving application problems is determined by
the success of the stage of their training. The task of learning neural networks is a complex …

Developing an accurate and reliable multi-step ahead prediction model is a key problem in
many Prognostics and Health Management (PHM) applications. Inevitably, the further one …

A reliable and accurate urban water demand forecasting plays a significant role in building
intelligent water supplying system and smart city. Due to the high frequency noise and …

Recurrent Neural Networks are powerful machine learning frameworks that allow for data to
be saved and referenced in a temporal sequence. This opens many new possibilities in …