期刊
ENTROPY
卷 22, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/e22050510
关键词
parameter estimation; total non-linear model; neural networks; neuro-computing; gradient descent algorithm
资金
- Prince Sultan University, Riyadh, Kingdom of Saudi Arabia
- Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia
- Prince Sultan University, Riyadh, Saudi Arabia
In this paper, a gradient descent algorithm is proposed for the parameter estimation of multi-input and multi-output (MIMO) total non-linear dynamic models. Firstly, the MIMO total non-linear model is mapped to a non-completely connected feedforward neural network, that is, the parameters of the total non-linear model are mapped to the connection weights of the neural network. Then, based on the minimization of network error, a weight-updating algorithm, that is, an estimation algorithm of model parameters, is proposed with the convergence conditions of a non-completely connected feedforward network. In further determining the variables of the model set, a method of model structure detection is proposed for selecting a group of important items from the whole variable candidate set. In order to verify the usefulness of the parameter identification process, we provide a virtual bench test example for the numerical analysis and user-friendly instructions for potential applications.
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