Abstract
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.
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