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...

وصف كامل

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	Lei Si, Zhongbin Wang, Xinhua Liu, Chao Tan, Jing Xu, Kehong Zheng
التنسيق:	مقال
اللغة:	English
منشور في:	MDPI AG 2015-11-01
سلاسل:	Sensors
الموضوعات:	shearer cutting condition identification parallel quasi-Newton algorithm neural network Dempster-Shafer theory feature extraction
الوصول للمادة أونلاين:	http://www.mdpi.com/1424-8220/15/11/28772

الانترنت

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

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

الانترنت

مواد مشابهة