LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

A novel shearer cutting pattern recognition model with chaotic gravitational search optimization

Photo by jjying from unsplash

Abstract The accurate recognition of the shearer cutting pattern is the focus in fully mechanized coal mining. Hence, a new cutting pattern recognition model based on the combination of Relevance… Click to show full abstract

Abstract The accurate recognition of the shearer cutting pattern is the focus in fully mechanized coal mining. Hence, a new cutting pattern recognition model based on the combination of Relevance Vector Machine (RVM) and Chaotic Gravitational Search Algorithm (CGSA) is proposed. Initially, the motor operation data, including voltage, current and motor speed, are collected as the detection signal and the RVM classifier based on Bayesian framework is chosen for pattern recognition. In order to optimize the parameters in RVM, which has a great influence on the performance of RVM, the optimization algorithm Gravitational Search Algorithm (GSA) is introduced. Finally, the basic GSA is modified into CGSA with the chaotic mapping for increasing the search diversity of the algorithm. The experimental study demonstrates the advantageous performance of the proposed model even without any feature extraction operations.

Keywords: gravitational search; model; cutting pattern; recognition; search; pattern recognition

Journal Title: Measurement
Year Published: 2019

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.