Abstract With the increasing complexity of large-scale industrial production processes, the number of variable factors is increasing. As a result, it is demanding to predict process key variables accurately. Currently, most of soft sensor models using support vector regression and artificial neural networks are based on point prediction. The soft measurement models using the technique of point prediction can only track or fit set values. It is difficult to deal with the problem of system uncertainty and to make reliability analysis using the point prediction based soft sensors. To address this problem, this paper proposes a development method of soft sensor using the technique of prediction intervals. Under this condition, the prediction intervals instead of the point prediction of the stable operation of the industrial process system are used. The interval boundaries of the trend change can be utilized to quantify and estimate the associated uncertainty. The proposed prediction intervals based soft sensor is based on fuzzy information granularity and improved recurrent extreme learning machine. First, the fuzzy information granularity is adopted to get the lower bound, trend and upper bound of the interval. Secondly, an improved recurrent extreme learning machine is built to further enhance the ability of prediction intervals. In the improved extreme learning machine model, a feedback layer is adopted to store the hidden layer output, calculate the data trend change and dynamically update the outputs of the feedback layer. Third, the comprehensive interval evaluation function is used to evaluate the rationality of the interval results. Through case studies using a University of California Irvine dataset and the purified Terephthalic acid solvent system, the provided prediction intervals method can directly generate the upper and lower bounds for process key variables with high accuracy.

中文翻译：

---

基于预测区间的软传感器开发使用模糊信息粒度和改进的循环 ELM

摘要 随着大规模工业生产过程的日益复杂，可变因素的数量也越来越多。因此，需要准确预测过程关键变量。目前，大多数使用支持向量回归和人工神经网络的软传感器模型都是基于点预测的。使用点预测技术的软测量模型只能跟踪或拟合设定值。使用基于点预测的软传感器难以处理系统不确定性问题和进行可靠性分析。针对这一问题，本文提出了一种利用预测区间技术的软传感器开发方法。在这种情况下，工业过程系统稳定运行的预测区间代替了点预测。趋势变化的区间边界可用于量化和估计相关的不确定性。所提出的基于预测区间的软传感器是基于模糊信息粒度和改进的循环极限学习机。首先，采用模糊信息粒度得到区间的下界、趋势和上界。其次，构建了改进的循环极限学习机，进一步增强了预测区间的能力。在改进的极限学习机模型中，采用反馈层存储隐藏层输出，计算数据趋势变化，动态更新反馈层输出。第三，综合区间评价函数用于评价区间结果的合理性。