基于范数优化超限学习机的矿浆浓度预测

选矿过程中的矿浆浓度是一个重要的生产工艺参数，一般可以通过预测矿浆浓度来提高生产效率。由于矿浆浓度和其他的生产工艺参数往往非线性相关，这给矿浆浓度的预测带来了很大困难。本文针对此问题，基于超限学习机这一面向神经网络的新颖学习算法，提出了一种矿浆浓度预测新算法。首先，使用相空间重构方法对矿浆浓度数据进行预处理，从一维转换到多维。然后，使用基于L2范数的超限学习机算法（ELM-L2）建立时序预测模型，实现预测功能。围绕来自于某矿厂的真实生产数据进行了实验验证，结果显示，针对大规模的数据样本集，所设计的算法与传统神经网络预测算法相比，训练时间大约减少了30%，而预测精度大约提高了48%。实验结果表明了所设计预测算法的有效性。

A prediction algorithm for pulp concentration using norm-optimized extreme learning machine

Pulp concentration as one of the most important production parameters plays an important role in the ore production. Generally, the production efficiency can be improved by a prediction for pulp concentration. Since there are some nonlinear relationships between the pulp concentration and other production parameters, it imposes very challenging obstacles to address this issue of prediction. A novel prediction method is proposed in this paper through the use of extreme learning machine (ELM) that is an effective learning algorithm developed for neural network. Firstly, the pulp concentration data is preprocessed by the phase space reconstruction method, and the time series prediction model is adjusted from one dimension to multiple dimensions. Secondly, an improved ELM algorithm using L2 norm (ELM-L2) is developed to implement the prediction. The experiments are conducted with a real-world production data set from a mine. Compared with the traditional prediction method using neural network, the proposed approach can reduce the training time by 30% and improve the prediction accuracy by 48% for a large-scale data set. The experimental results show the effectiveness of the proposed algorithm.