基于矢量基学习的浸出过程在线建模

传统的支持向量回归算法因基于批量训练方法而无法适应浸出过程在线建模实时性的要求.在分析研究一种基于矢量基学习的支持向量回归算法的基础上,提出了基于矢量基学习的浸出过程在线建模方法.利用贝叶斯证据框架优化模型参数,分析新样本矢量与矢量空间的夹角,从而推导出该样本是否为基矢量.将该方法应用于浸出过程浸出率的预测,实验结果表明,该方法不但能很好地跟踪浸出率的变化趋势,而且显著地缩短了运算时间.

Online modeling method for leaching process based on vector base learning

Traditional support vector regression(SVR) algorithm based on batch training can’t satisfy the real-time
requirement of online modeling for leaching process. Therefore, with the analysis of a support vector regression algorithm
based on the vector base learning, an online modeling method for leaching process is proposed. Bayesian evidence framework
is used to optimize the model parameters. By calculating the angle between the new sample vector and the vector space, the
criteria for determining whether the measurement vector is one of the BVS is derived. The method is adopted in the prediction
of leaching rate, and tested by experiment. The experiment result shows that the proposed method can track the trend of the
leaching rate, and reduce the operation time effectively.