- author = "Peter Marenbach",
- title = "Using Prior Knowledge and Obtaining Process Insight in Data Based Modelling of Bioprocesses",
- journal = "System Analysis Modelling Simulation",
- year = "1998",
- howpublished = "Overseas Publishers association",
- volume = "31",
- number = "1-2",
- month = jan,
- pages = "39--59",
- note = "Special issue on automatic model generation",
- keywords = "genetic algorithms, genetic programming, biotechnology, bioprocesses, data based modelling, SMOG",
-
URL = "
https://www.sciencebase.gov/catalog/item/50536091e4b097cd4fcd67f7",
- broken = "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/publications.html#SAMS98",
-
URL = "http://dl.acm.org/citation.cfm?id=289662&CFID=140128841&CFTOKEN=57960727",
- ISSN = "0232-9298",
- acmid = "289662",
- publisher = "Gordon and Breach Science Publishers, Inc.",
- address = "Newark, NJ, USA",
- 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.",
-
notes = "
Reprints available from P. Marenbach.",
