Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory.

Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Xu, Jing; Zheng, Kehong

Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Xu, Jing; Zheng, Kehong.

Afiliação

Si L; School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China. lei.si@cumt.edu.cn.
Wang Z; School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China. lei.si@cumt.edu.cn.
Liu X; School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China. wangzbpaper@126.com.
Tan C; School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China. l_xinhua_2006@126.com.
Xu J; School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China. tccadcumt@126.com.
Zheng K; School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China. xujingcumt@126.com.

Sensors (Basel) ; 15(11): 28772-95, 2015 Nov 13.

Article em En | MEDLINE | ID: mdl-26580620

Palavras-chave

Dempster-Shafer theory; cutting condition identification; feature extraction; neural network; parallel quasi-Newton algorithm; shearer

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2015 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

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