scholarly journals An Ensemble Deep Belief Network Model Based on Random Subspace for NOx Concentration Prediction

ACS Omega ◽  
2021 ◽  
Vol 6 (11) ◽  
pp. 7655-7668
Author(s):  
Yingnan Wang ◽  
Guotian Yang ◽  
Ruibiao Xie ◽  
Han Liu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Network Model ◽  
Deep Belief Network ◽  
Random Subspace ◽  
Belief Network ◽  
Model Based ◽  
Concentration Prediction

An ontology-based deep belief network model

Computing ◽  
2021 ◽  
Author(s):  
Xiulei Liu ◽  
Ruoyu Chen ◽  
Qiang Tong ◽  
Zhihui Qin ◽  
Qinfu Shi ◽  
...  
Keyword(s):  
Network Model ◽  
Deep Belief Network ◽  
Belief Network

scholarly journals A Deep Belief Network Combined with Modified Grey Wolf Optimization Algorithm for PM2.5 Concentration Prediction

2019 ◽  
Vol 9 (18) ◽  
pp. 3765 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Chuang Chen ◽  
Yunfei Xiang ◽  
Yang Chen ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Fitness Function ◽  
Deep Belief Network ◽  
Structure Parameters ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Belief Network ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Accurate PM2.5 concentration prediction is crucial for protecting public health and improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2.5 concentration prediction has received increasing attention due to its effectiveness. However, the DBN structure parameters that have a significant impact on prediction accuracy and computation time are hard to be determined. To address this issue, a modified grey wolf optimization (MGWO) algorithm is proposed to optimize the DBN structure parameters containing number of hidden nodes, learning rate, and momentum coefficient. The methodology modifies the basic grey wolf optimization (GWO) algorithm using the nonlinear convergence and position update strategies, and then utilizes the training error of the DBN to calculate the fitness function of the MGWO algorithm. Through the multiple iterations, the optimal structure parameters are obtained, and a suitable predictor is finally generated. The proposed prediction model is validated on a real application case. Compared with the other prediction models, experimental results show that the proposed model has a simpler structure but higher prediction accuracy.

scholarly journals An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 87593-87605 ◽  
Author(s):  
Peng Wei ◽  
Yufeng Li ◽  
Zhen Zhang ◽  
Tao Hu ◽  
Ziyong Li ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Optimization Method ◽  
Deep Belief Network ◽  
Classification Model ◽  
Belief Network ◽  
Model Based
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