CMD: controllable matrix decomposition with global optimization for deep neural network compression

2022 ◽  
Author(s):  
Haonan Zhang ◽  
Longjun Liu ◽  
Hengyi Zhou ◽  
Hongbin Sun ◽  
Nanning Zheng
Keyword(s):  
Neural Network ◽  
Global Optimization ◽  
Deep Neural Network ◽  
Matrix Decomposition ◽  
Network Compression

Low Rank Based End-to-End Deep Neural Network Compression

2021 ◽  
Author(s):  
Swayambhoo Jain ◽  
Shahab Hamidi-Rad ◽  
Fabien Racape
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Low Rank ◽  
End To End ◽  
Network Compression

scholarly journals Deep Neural Network Compression for Aircraft Collision Avoidance Systems

2019 ◽  
Vol 42 (3) ◽  
pp. 598-608 ◽  
Author(s):  
Kyle D. Julian ◽  
Mykel J. Kochenderfer ◽  
Michael P. Owen
Keyword(s):  
Neural Network ◽  
Collision Avoidance ◽  
Deep Neural Network ◽  
Network Compression

A study on global optimization and deep neural network modeling method in performance-seeking control

2019 ◽  
Vol 234 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Qiangang Zheng ◽  
Dawei Fu ◽  
Yong Wang ◽  
Haoying Chen ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Global Optimization ◽  
Interval Analysis ◽  
Sequential Quadratic Programming ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Engine Performance ◽  
Neural Network Modeling ◽  
Swarm Optimization

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

Constraint-Aware Deep Neural Network Compression

2018 ◽  
pp. 409-424 ◽  
Author(s):  
Changan Chen ◽  
Frederick Tung ◽  
Naveen Vedula ◽  
Greg Mori
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Compression
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