Bioprocess Modeling Using Fuzzy Regression Clustering and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Wu:2006:WCICA,

• author = "Yanling Wu and Jiangang Lu and Jian Xu and Youxian Sun",
• title = "Bioprocess Modeling Using Fuzzy Regression Clustering and Genetic Programming",
• booktitle = "The Sixth World Congress on Intelligent Control and Automation, WCICA 2006",
• year = "2006",
• volume = "2",
• pages = "9337--9341",
• month = "21-23 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "1-4244-0332-4",
• 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.",
• notes = "National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; Department of automation, Anhui University, Hefei 230039, China.",

}

Genetic Programming entries for Yanling Wu Jiangang Lu Jian Xu Youxian Sun

Citations