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

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

In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and cu...

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Bibliographic Details
Main Authors: Lei Si, Zhongbin Wang, Xinhua Liu, Chao Tan, Jing Xu, Kehong Zheng
Format: Article
Language:English
Published: MDPI AG 2015-11-01
Series:Sensors
Subjects:
shearer
cutting condition identification
parallel quasi-Newton algorithm
neural network
Dempster-Shafer theory
feature extraction
Online Access:http://www.mdpi.com/1424-8220/15/11/28772

Internet

http://www.mdpi.com/1424-8220/15/11/28772

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