Prediction of nitrogen oxide emission concentration in cement production process: a method of deep belief network with clustering and time series

2021 ◽  
Author(s):  
Xiaochen Hao ◽  
Qingquan Xu ◽  
Xin Shi ◽  
Zhixing Song ◽  
Yakun Ji ◽  
...  
Keyword(s):  
Time Series ◽  
Production Process ◽  
Nitrogen Oxide ◽  
Deep Belief Network ◽  
Nitrogen Oxide Emission ◽  
Cement Production ◽  
Belief Network

scholarly journals Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting

2016 ◽  
pp. 30-37 ◽  
Author(s):  
Takaomi Hirata ◽  
Takashi Kuremoto ◽  
Masanao Obayashi ◽  
Shingo Mabu ◽  
Kunikazu Kobayashi
Keyword(s):  
Time Series ◽  
Reinforcement Learning ◽  
Time Series Forecasting ◽  
Deep Belief Network ◽  
Belief Network

scholarly journals A Cycle Deep Belief Network Model for Multivariate Time Series Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Shuqin Wang ◽  
Gang Hua ◽  
Guosheng Hao ◽  
Chunli Xie
Keyword(s):  
Time Series ◽  
Network Model ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Deep Belief Network ◽  
Learning Ability ◽  
Series Data ◽  
Temporal Data ◽  
Belief Network ◽  
Data Objects

Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.

scholarly journals Chaotic time series prediction with deep belief networks: an empirical evaluation

2020 ◽  
Vol 3 (SI1) ◽  
pp. SI102-SI112
Author(s):  
Duong Tuan Anh ◽  
Ta Ngoc Huy Nam
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Real World ◽  
Initial Conditions ◽  
Empirical Evaluation ◽  
Chaotic Time Series ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Rbf Network

Chaotic time series are widespread in several real world areas such as finance, environment, meteorology, traffic flow, weather. A chaotic time series is considered as generated from the deterministic dynamics of a nonlinear system. The chaotic system is sensitive to initial conditions; points that are arbitrarily close initially become exponentially further apart with progressing time. Therefore, it is challenging to make accurate prediction in chaotic time series. The prediction using conventional statistical techniques, k-nearest-nearest neighbors algorithm, Multi-Layer-Perceptron (MPL) neural networks, Recurrent Neural Networks, Radial-Basis-Function (RBF) Networks and Support Vector Machines, do not give reliable prediction results for chaotic time series. In this paper, we investigate the use of a deep learning method, Deep Belief Network (DBN), combined with chaos theory to forecast chaotic time series. DBN should be used to forecast chaotic time series. First, the chaotic time series are analyzed by calculating the largest Lyapunov exponent, reconstructing the time series by phase-space reconstruction and determining the best embedding dimension and the best delay time. When the forecasting model is constructed, the deep belief network is used to feature learning and the neural network is used for prediction. We also compare the DBN –based method to RBF network-based method, which is the state-of-the-art method for forecasting chaotic time series. The predictive performance of the two models is examined using mean absolute error (MAE), mean squared error (MSE) and mean absolute percentage error (MAPE). Experimental results on several synthetic and real world chaotic datasets revealed that the DBN model is applicable to the prediction of chaotic time series since it achieves better performance than RBF network.

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