In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, original energy signal is decomposed by CEEMDAN algorithm obtain several eigenmode function components and residual components; functions are fed into NARX network for computational purposes. The partial hypothesis superimposed in following part final forec...

The temporal evolution of nearshore sandbars (alongshore ridges of sand fringing coasts in water depths less than 10 m and of paramount importance for coastal safety) is commonly predicted using process-based models. These models are autoregressive and require offshore wave characteristics as input, properties that find their neural network equivalent in the NARX (Nonlinear AutoRegressive model...

The formation of protein secondary structure especially the regions of β-sheets involves long-range interactions between amino acids. We propose a novel recurrent neural network architecture called Segmented-Memory Recurrent Neural Network (SMRNN) and present experimental results showing that SMRNN outperforms conventional recurrent neural networks on long-term dependency problems. In order to ...

The turbofan engine is a pivotal component of the aircraft. Engine components are susceptible to degradation over life their operation, which affects reliability and performance an engine. In order direct necessary maintenance behavior, remaining useful prediction key. This research uses machine learning provide framework for aircraft’s (RUL) based on entire cycle data deterioration parameter (...

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include la...

We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure. We explain efficient inference procedures that allow application to both parsing and language modeling. Experiments show that they provide better parsing in English than any single previously published supervised generative model and better language modeling than state-of-the-art se...