Deep Neural Network Learning of Model Predictive Control Considering Dynamic Constraint

2017 ◽  
Vol 2017 (0) ◽  
pp. 1P1-I12
Author(s):  
Daichi FURUTA ◽  
Kyo KUTSUZAWA ◽  
Tetsugaku OKAMOTO ◽  
Sho SAKAINO ◽  
Toshiaki TSUJI
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Deep Neural Network ◽  
Neural Network Learning ◽  
Network Learning ◽  
Dynamic Constraint

Aero-engine direct thrust control with nonlinear model predictive control based on linearized deep neural network predictor

2019 ◽  
Vol 234 (3) ◽  
pp. 330-337 ◽  
Author(s):  
Qiangang Zheng ◽  
Yong Wang ◽  
Fengyong Sun ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Deep Neural Network ◽  
Sliding Window ◽  
Nonlinear Model Predictive Control ◽  
Thrust Control ◽  
Direct Thrust Control ◽  
Linearized Model

A novel nonlinear model predictive control method for aero-engine direct thrust control is proposed to improve engine response ability and reduce computational complexity of nonlinear model predictive control. The control objective of the proposed method is the thrust directly instead of the measurable parameters. The linearized model based on online sliding window deep neural network is proposed as predictive model. The online sliding window deep neural network has strong fitting capacity for nonlinear object and adopted to fitting the transient process of engine. The back propagation is adopted to obtain linearized model of online sliding window deep neural network, which greatly reduce the calculated amount. The comparison simulations of the popular nonlinear model predictive control based on extended Kalman filter and the proposed one are carried out. The simulation results show that compared with the popular nonlinear model predictive control, the proposed nonlinear model predictive control not only has the better response ability but also has reduced computational complexity greatly, nearly reduce computation time more than 35 ms.

scholarly journals Predictive Model for Brain Stroke in CT using Deep Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 2011-2017
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Principal Component ◽  
World Population ◽  
Neural Network Learning ◽  
Machine Learning Classification ◽  
Network Learning ◽  
Brain Stroke ◽  
Aging World

The increasing in the incidence of stroke with aging world population would quickly place an economic burden on society. In proposed method we use different machine learning classification algorithms like Decision Tree, Deep Neural Network Learning, Maximum Expectization , Random Forest and Gaussian Naïve Bayesian Classifier is used with associated number of attributes to estimate the occurrence of stroke disease. The present research, mainly PCA (Principal Component Analysis) algorithm is used to limit the performance and scaling used to be adopted to extract splendid context statistics from medical records. We used those reduced features to determine whether or not the patient has a stroke disorder. We compared proposed method Deep neural network learning classifier with other machine-learning methods with respect to accuracy, sensitivity and specificity that yields 86.42%, 74.89 and 88.49% respectively. Hence it can be with the aid of both patients and medical doctors to treat viable stroke.

Statistical insights into deep neural network learning in subspace classification

Stat ◽  
2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Hao Wu ◽  
Yingying Fan ◽  
Jinchi Lv
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Learning ◽  
Network Learning
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