Judgment for Quality of Sintered Ore Based on Neural Network

摘要

Focusing on the problem in production practice of sintering process, a novel classifier based on BP learning algorithm is proposed for on-line quality inference of sintered ore. In order to speed up the convergence rate of BP learning algorithm, the learning algorithm with adaptive variable step-size is adopted. On the basis of the above work a quality prediction model is proposed in this paper. Experimental results indicate the higher prediction accuracy rate and better generalization ability of the model.