Abstract
Abstract Projection to Latent Structures (PLS) is a linear regression technique for nondynamic problems where the data is noisy, highly correlated and where there are a limited number of observations. Methodologies have been proposed to integrate the nonlinear features within a linear PLS framework resulting in a non-linear algorithm. PLS has also been extended to include dynamic processes. This paper presents a non-linear dynamic PLS algorithm which incorporates polynomial or neural network functions that are integrated within the PLS algorithm through weight updating of the inner/outer models. The modelling capabilities are assessed through comparisons on a pH neutralisation process.
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