Abstract
AbstractThis paper introduces the concept of sample study risk in neural network (NN), and studies the prediction of hydrogen content in coal using Back Propagation Neural Networks (BP NN). Targeting the problem of training convergence quality impaired by the interfering information of some samples in BP NN, the validity of the concept of sample study in NN, and the feasibility of analyzing chemical elements in coal using NN are discussed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Developments in Chemical Engineering and Mineral Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
