Research and chemical application of data feature extraction based AANN-ELM neural network

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.