A study on global optimization and deep neural network modeling method in performance-seeking control
| Autor: | Yong Wang, Haibo Zhang, Qiangang Zheng, Dawei Fu, Haoying Chen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Neural network modeling Computer science business.industry Mechanical Engineering Computer Science::Neural and Evolutionary Computation 020208 electrical & electronic engineering Control (management) 02 engineering and technology Interval arithmetic 020901 industrial engineering & automation Control and Systems Engineering Global optimization algorithm 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Global optimization Control methods |
| Zdroj: | Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 234:46-59 |
| ISSN: | 2041-3041 0959-6518 |
| DOI: | 10.1177/0959651819852477 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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