Abstract

AbstractStarting from an equivalent presentation of projection to latent structures (PLS), a novel nonlinear PLS approach is presented where both nonlinear latent structures and nonlinear reconstruction are obtained straightforwardly through two consecutive steps. First, an radial basis functions (RBF) network is utilized to extract the latent structures through linear algebra methods without the need of nonlinear optimization. This is followed by two feed-forward networks (FFN) to reconstruct both the original predictor variables and response variables. The proposed algorithm exhibits fast convergence speed and its efficiency is assessed through both mathematical example and modelling of a pH neutralization process.KeywordsHide LayerRadial Basis FunctionLatent StructureRadial Basis FunctionProjection StepThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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