小波核极限学习机及其在醋酸精馏软测量建模中的应用

传统的机器学习算法一般通过迭代进行参数寻优，导致学习速度慢，且容易陷入局部最小值。针对这个问题，提出了一种基于小波核函数的极限学习机（KEML）的软测量建模方法，将支持向量机（SVM）中核函数的思想运用到极限学习机（EML）中,避免了SVM训练速度慢以及ELM算法不稳定的缺点。将KEML算法运用于醋酸精馏的软测量建模问题中，仿真实验结果验证了该算法的学习速度是SVM的92倍，且算法的精度以及模型的泛化能力都有所提高。

Wavelet Kernel Extreme Learning Machine and Its Application in Soft Sensor Modeling of an Industrial Acetic Acid Distillation System

Traditional machine learning algorithms usually adopt iterating method to achieve the parameter optimization, which may lead to slow learning speed and easy falling into local minimum. The paper proposes a soft measurement modeling method based on extreme learning machine with wavelet kernel function (KEML). By applying the idea of the kernel function in support vector machine (SVM) to the extreme learning machine (EML), the proposed algorithm can overcome the slow training speed of SVM and the unstability of ELM algorithm. The experimental results by applying this method to the acetic acid distillation of soft measurement model show that the learning speed of KEML is 92 times as SVM, and the accuracy and generalization ability of the model is also improved.