A nonlinear PLS modeling method based on extreme learning machine

This paper proposes a nonlinear PLS modeling method. The extreme learning machine (ELM) is embedded in the process of linear PLS modeling. Thus the linear PLS modeling is transformed into the nonlinear frame which can deal with nonlinear data. The multi-input-multi-output (MIMO) nonlinear modeling task is decomposed into two parts: the external linear modeling and inner univariate nonlinear modeling problems. The linear PLS method is used to establish the external model, while the extreme learning machine is used to capture the inner nonlinear model. Compared to the standard PLS method, the method in this paper has the potential of modeling any continuous nonlinear relationship and has better robust properties. And it is less time-consuming than other neural networks PLS (NNPLS) methods. Because extreme learning machine can capture the inner nonlinearity of data, the proposed method has better prediction performance than linear PLS regression method. Simulation verifies the better prediction performance and the validity of the proposed method.