Bioprocess Modeling Using Fuzzy Regression Clustering and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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