Nonlinear system modeling with deep neural networks and autoencoders algorithm
Erick de la Rosa, Wen Yu, Xiaoou Li · 2016
Deep learning techniques have been successfully used for pattern classification. These advantage methods are still not applied in nonlinear systems identification. In this paper, the neural model has deep architecture which is obtained by a random search method. The initial weights of this deep neural model is obtained from the denoising autoencoders model. We propose special unsupervised learning methods for this deep learning model with input data. The normal supervised learning is used to train the weights with the output data. The deep learning identification algorithms are validated with three benchmark examples.