基于双阈值AdaBoost算法的4-CBA含量软测量建模

针对PX氧化过程中4-CBA含量无法在线测量的问题，提出了一种基于双阈值更新样本权重的AdaBoost算法，该算法以BP神经网络作为弱学习器，采用轮盘赌方法根据样本权重在训练样本集中选择部分样本训练弱学习器，采用上一轮弱学习器的训练相对误差绝对值来更新所有训练样本的权重，在此基础上，用双阈值对样本误差范围进行划分，然后用不同的权重因子与原来的样本权值相乘实现样本权值的二次更新。该过程降低了含有大误差的样本的权值，增加了较大误差的样本的权值，从而减小了在下一轮训练过程中选到异常样本的概率。分别采用5种不同的方法并用实测的工业数据建立了4-CBA含量软测量模型，仿真结果表明用提出的改进AdaBoost算法建立的4-CBA含量软测量模型，其预测误差小于其他方法建立的模型误差。

Modeling soft sensor of 4-CBA concentration by AdaBoost algorithm with dual threshold technique

A modified AdaBoost algorithm with updating sample weight by dual threshold technique was proposed to model a soft sensor for estimating 4-CBA concentration, which could not be measured on-line in PX oxidation process. In this method, weak learners of BP neural networks were trained by part of samples selected by their weights and roulette wheel mechanism. The absolute values of last round training relative errors in weak learners were adopted to update weights of all training samples. Then, a second round updating on sample weights were completed by the product of original sample value and its weighting factor, which was defined by ratio of error range over dual thresholds. In the second updating process, weights were decreased for samples with gross errors but were increased for those with medium error. Consequently, probability of selecting outliers was reduced in following iteration of the training process. Five different methods were applied to model soft sensor of 4-CBA concentration with industrial data. Simulation results showed that the modified AdaBoost algorithm can improve soft sensor performance of 4-CBA concentration with predicting error less than that of other models.