Soft sensor development using PLSR based multi-kernel ELM

It takes many efforts to establish accurate soft sensor models because of the increasing complication of processes. For the sake of solving this problem, a novel multi-kernel extreme learning machine based on partial least square regression (PLSR) is proposed. In the proposed method, different kernel functions are used for mapping the space of process data to highly nonlinear space. The partial least square regression is adopted to obtain the relationship between the nonlinear space and output layer. To validate the performance of the proposed model, a case study using the High Density Polyethylene process is executed. Simulation results confirm the performance of the proposed model.