Complex Nonlinear System Modelling and Parameters Identification by Deep Neural Networks

Hai-long LIN, Gao-yong LUO, Hai-tao CAO, Xiao-hui FANG, Fa-sheng ZHOU


As most systems are inherently nonlinear in nature, many efforts have been made to improve the understanding of complicated nonlinear models. However, current research has indicated that it is still a challenge to accurately model and identify nonlinear systems by conventional methods such as machine learning. This paper investigates a complex nonlinear system with three parameters identification by training a Deep Neural Network (DNN) to model the system based on Fourier series theory. The DNN with 10 layers is constructed such that it can model any nonlinear system, and the parameters identification is performed by the trained neural networks. The proposed method has been evaluated by applying to a nonlinear system for multiple parameters measurement by interferometric fiber sensors. Experimental results demonstrate that the DNN can accurately model the nonlinear system and identify the corresponding parameters, leading to a solution to complex nonlinear system approximation with minimized error.


Complex nonlinear system, Deep neural networks, Parameters identification, Machine learning


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