E3S Web Conf.
Volume 162, 2020The 4th International Conference on Power, Energy and Mechanical Engineering (ICPEME 2020)
|Number of page(s)||7|
|Section||Power and Energy Engineering|
|Published online||07 April 2020|
Dynamic Prediction of the Thermal Nonlinear Process Based on Deep Hybrid Neural Network
Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing, 210096, Jiangsu Province, China
* Corresponding author: firstname.lastname@example.org
Nonlinear system prediction plays an important role in the practical thermal process, and deep learning algorithm is now popular in nonlinear dynamic system modeling because of its powerful learning ability. In this paper, the dynamic artificial neural networks (DANNs), which can be divided into two different types with external dynamic characteristics and internal dynamic characteristics, are analyzed. The mathematical formulations of feedforward deep neural network (DNN), traditional recurrent neural network (RNN) and Long-Short Term Memory network (LSTM) models are given. Furthermore, the structure of deep Hybrid Neural Network (DHNN) is described. Finally, the applicability of the above models in the thermal nonlinear process with different structural features is discussed. Simulation experiments reveal that DANNs with internal dynamic characteristics more suitable for solving thermal nonlinear system modeling problems with unknown order, and DHNN based on LSTM model has performed much better in approximating the dynamics of the thermal process with state parameters.
© The Authors, published by EDP Sciences, 2020
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