基于高斯过程和改进教与学优化算法的甲醇合成转化率软测量  

Soft Sensor of Conversion Rate in Methanol Synthesis Based on Gaussian Process and Improved Teaching-Learning-Based Optimization

作　　者：双翼帆 张凌波[1] 顾幸生[1] 

机构地区：[1]华东理工大学化工过程先进控制与优化技术教育部重点实验室,上海200237

出　　处：《华东理工大学学报（自然科学版）》2016年第3期369-374,共6页Journal of East China University of Science and Technology

基　　金：国家自然科学基金(61174040);上海市科委基础研究重点项目(12JC1403400)

摘　　要：针对高斯过程软测量建模过程中,常用的共轭梯度法难以完成高维协方差矩阵的超参数确定等问题,引入了教与学优化算法(TLBO)对高斯过程的训练过程进行了优化,提高了模型训练速度。并对基本的教与学优化算法做出了相应的改进:一是改进了算法的"学生阶段";二是增加了"课外阅读阶段",提高了算法的性能。将这一建模方法应用于甲醇合成转化率测量中,结果表明,该方法具有较好的估计精度。During the training process of Gaussian process, the commonly used conjugate gradient method is difficult to handle the hyper-parameter of the high-dimensional covariance matrix. Aiming at this problem,this paper introduced the teaching-learning-based optimization （TLBO） algorithm to accelerate the training process of Gaussian process. However, the basic TLBO has local convergence phenomena in certain conditions. This paper proposed an improved TLBO （ITLBO） algorithm by modifying the learner phase and appending an outside-reading phase to increase the population diversity so as to improve the global searching ability. Finally this method is applied to measure the methanol conversion rate and the results indicate that the proposed method has a good result and a certain value.

关 键 词：高斯过程 软测量 甲醇转化率 建模 教与学优化算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]