Manufacturing modeling using an evolutionary fuzzy regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Chan:2011:ieeeFUZZ,

• author = "K. Y. Chan and T. S. Dillon and S. H. Ling and C. K. Kwong",
• title = "Manufacturing modeling using an evolutionary fuzzy regression",
• booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ 2011)",
• year = "2011",
• month = "27-30 " # jun,
• address = "Taipei, Taiwan",
• pages = "2261--2267",
• size = "7 pages",
• abstract = "Fuzzy regression is a commonly used approach for modelling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and nonlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modelling of epoxy dispensing process is carried out.",
• keywords = "genetic algorithms, genetic programming, evolutionary fuzzy regression, fuzzy coefficients, manufacturing modelling, manufacturing process model, fuzzy set theory, manufacturing processes, regression analysis",
• DOI = "doi:10.1109/FUZZY.2011.6007322",
• ISSN = "1098-7584",
• notes = "Also known as \cite{6007322}",

}

Genetic Programming entries for Kit Yan Chan Tharam S Dillon Sing Ho Ling Che Kit Kwong

Citations