Evolutionary versus inductive construction of neurofuzzy systems for bioprocess modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{marenbach:1997:Evicnf,

• author = "P. Marenbach and M. Brown",
• title = "Evolutionary versus inductive construction of neurofuzzy systems for bioprocess modelling",
• booktitle = "Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA",
• year = "1997",
• editor = "Ali Zalzala",
• address = "University of Strathclyde, Glasgow, UK",
• publisher_address = "Savoy Place, London WC2R 0BL, UK",
• month = "1-4 " # sep,
• publisher = "Institution of Electrical Engineers",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-85296-693-8",
• URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_97_11.pdf",
• URL = "http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=681045",
• DOI = "doi:10.1049/cp:19971200",
• size = "6 pages",
• abstract = "The control and optimization of biotechnological processes is a complex task of industrial relevance, due to the growing importance attached to biotechnology. Therefore, there is an increasing use of intelligent data analysis methods for the development and optimization of bioprocess modelling and control. Since a clear understanding of the underlying physics does not exist, nonlinear learning systems, which can accurately model exemplar data sets and explain their behaviour to the designer, are an attractive approach. This paper investigates applying neurofuzzy construction algorithms to this problem and in particular compares a Genetic Programming structuring approach with a more conventional forwards inductive learning-type algorithm. It is shown that for simple problems, the inductive learning technique generally outperforms the Genetic Programming, although for large complex problems, the latter may prove beneficial.",
• notes = "GALESIA'97",

}

Genetic Programming entries for Peter Marenbach M Brown

Citations