Abstract: The extreme learning machine usually exist the problems on high-dimensional data modeling in chemical process.Aiming at solving these problems,the auto-associative neural network is combined,in which the auto-associative neural network is constructed to filter redundant information and extract characteristic components,and these characteristic components are trained by extreme learning machine.Thus,a data feature extraction based auto-associative neural network-extreme learning machine(AANN-ELM)is formed.Meanwhile,the effectiveness of this network is verified by the UCI standard data sets and the purified terephthalic acid(PTA)solvent system.The result indicates that AANN-ELM has the characteristics of fast learning speed,stable network output,and high model precision in handling with high-dimensional data,which will provide a new way to apply the neural network in complex chemical production.

Key words: extreme learning machine, auto-associative neural network, high-dimensional data, process modeling

摘要： 针对极限学习机不能有效解决化工过程中高维数据建模的问题,本文将其与自联想神经网络结合,通过自联想神经网络过滤输入数据中存在的冗余信息、提取特征分量,并对所提取的特征分量采用极限学习机进行训练,由此形成了一种基于数据特征提取的AANN-ELM(auto-associative neural network-extreme learning machine)神经网络。同时,以UCI标准数据集进行测试,以精对苯二甲酸(PTA)溶剂系统进行验证,结果表明,AANN-ELM在处理高维数据时具有学习速度快、网络稳定性强、建模精度高的特点,为神经网络在复杂化工生产中的应用提供了新思路。

关键词: 极限学习机, 自联想神经网络, 高维数据, 过程建模