Article,
Using Prior Knowledge and Obtaining Process Insight in Data Based Modelling of Bioprocesses
System Analysis Modelling Simulation, (1998)
Abstract
In biotechnology, as is many other fields of
technology, the development of a appropriate process
model is one of the most important engineering tasks.
Data driven modelling becomes an attractive approach
whenever analytical modelling is difficult or too time
consuming. Disadvantages of the often used artificial
neural networks are their missing transparency and the
difficulty to integrate prior knowledge. The paper at
hand gives an overview of several common modelling
techniques with focus on their application to
bioprocesses and presents a novel modelling technique
that uses genetic programming for the construction and
refinement of transparent structured models.
