Identification Random Disturbances of Optomechanical Control Systems Based Neural Observers
| Autor: | Nguyen Duc Thanh, Mikhail P. Belov, Nguyen Van Lanh |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mean squared error
Observer (quantum physics) Computer science Stochastic process 020209 energy Process (computing) 02 engineering and technology Identification (information) Recurrent neural network Control theory Control system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing MATLAB computer computer.programming_language |
| Zdroj: | 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). |
| DOI: | 10.1109/eiconrus49466.2020.9039524 |
| Popis: | This paper deals with the application of neural observer to estimate external random disturbance in the form of wind gust dynamic acting on the opto-mechanical control systems. First, we introduce the mathematical model of wind gust disturbances, which are considered as random processes with zero mean. The next section investigates architecture of neural observer based on dynamic recurrent neural networks (DRNN). The learning process of DRNN is described and the performances mean square error (MSE) of different learning algorithms are compared with the aim of studying quality identification and selection of the best learning algorithm for synthesis neural observer. In this work, designing, training, testing of DRNN is carried out in Matlab/ SIMULINK environment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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