Bioprocess Modeling Using Fuzzy Regression Clustering
and Genetic Programming
The Sixth World Congress on Intelligent Control and
Automation, WCICA 2006, 2, page 9337--9341. IEEE, (21-23 June 2006)DOI: doi:10.1109/WCICA.2006.1713808
Abstract
In an industrial Avermection bioprocess, there are
some different phases and some interims, so using fuzzy
clustering technique to partition the whole process and
using several models to represent these different
phases respectively is more reasonable. Here, we built
a fuzzy model for the bioprocess by a mixture method
which integrates fuzzy regression clustering technique,
genetic programming (GP), genetic algorithm (GA) and
interpolation technique. Fuzzy regression clustering
technique is used to partition the whole input space
into several subspaces based on whether the training
data having a similar model which is identified by GP,
GA is used to optimises the parameters of these models
and interpolation technique used to define the
membership grade for the input data. By this approach,
we can fuzzily partition the whole process, find the
structures of models which represent subspaces
respectively and estimate the parameters
simultaneously. Moreover, it has more chance to get a
solution with better generalisation.