Abstract
The issue of outer model weight updating is important
in extending partial least squares (PLS) regression to
modelling data that shows significant non-linearity.
This paper presents a novel co-evolutionary component
approach to the weight updating problem. Specification
of the non-linear PLS model is achieved using an
evolutionary computational (EC) method that can
co-evolve all non-linear inner models and all input
projection weights simultaneously. In this method,
modular symbolic non-linear equations are used to
represent the inner models and binary sequences are
used to represent the projection weights. The approach
is flexible, and other representations could be
employed within the same co-evolutionary framework. The
potential of these methods is illustrated using a
simulated pH neutralisation process data set exhibiting
significant non-linearity. It is demonstrated that the
co-evolutionary component architecture can produce
results which are competitive with non-linear neural
network-based PLS algorithms that use iterative
projection weight updating. In addition, a data
sampling method for mitigating overfitting to the
training data is described
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