Nonlinear system identification using deep belief network based on PLSR

Deep learning has been successfully applied into pattern recognition due to its deep architecture and effective unsupervised learning, and deep belief network (DBN) is a popular model based on deep learning technique. In this paper, a DBN identification model based on partial least square regression (PLSR), named PLSR-DBN, is proposed for nonlinear system identification. In order to improve the identification accuracy, PLSR is introduced into the supervised fine-tuning of DBN to elimate the overfitting and local minimum resulted from gradients-based learning, and contrastive divergence (CD) algorithm is used in unsupervised pre-training. Finally, the proposed PLSR-DBN is tested on a benchmark nonlinear system. The experiment results show that the proposed PLSR-DBN has a better performance on nonlinear system identification than other similar methods.