An improved extreme learning machine integrated with nonlinear principal components and its application to modeling complex chemical processes

Abstract

In order to enhance the performance of extreme learning machine (ELM) in modeling complex chemical processes, an improved ELM integrated with nonlinear principal components is proposed. Firstly, an improved ELM (IELM) model is presented. The IELM has a special structure with two independent input subnets: a positive correlation subnet and a negative correlation subnet. The two independent input subnets are developed based on the correlation coefficient between input attributes and output attributes. The nonlinear principal components of original input attributes are extracted using input training neural network (ITNN). The extracted nonlinear principal components are connected to output layer nodes. Thus, the output nodes not only connect with the positive correlation subnet and the negative correlation subnet, but also with the extracted nonlinear principal components. Thus, an IELM integrated with nonlinear principal components (NPCs-IELM) model can be built. The effectiveness of the proposed NPCs-IELM is verified by modeling a high density polyethylene process. Simulation results indicate that the proposed NPCs-IELM can achieve higher accuracy and better stability.