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《第36届中国控制会议论文集(G)》2017年
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Nonlinear system identification using deep belief network based on PLSR

Wang Gongming  Li Wenjing  Qiao Junfei  Wu Guandi  
【摘要】: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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