A Nonlinear Partial Least Square Modeling Method Based on Gaussian Process

A new nonlinear partial least squares method(GP-PLS) based on Gaussian process is(proposed) to deal with complicated processes with nonlinearities and a large number of correlated inputs.The GP-PLS method,which has merits of both GP and PLS,is an integration of GP models and partial least squares.The PLS outer projection is used as a dimension reduction tool to remove collinearity and the GP models are trained to capture the nonlinearities in the projected latent space.Soft sensor modeling of acrylonitrile yield using GP-PLS method is established.It is found that the generalization ability and the accuracy of the soft sensor using the method proposed are superior to traditional methods,and the performance of the soft sensor meets the demands of industrial application in the field.