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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chan, K.Y. Dillon, T.S. Ling, S.H. Kwong, C.K. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong (Kwong, C.K.) || Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, Perth, Australia (Chan, K.Y.; Ling, S.H.) || Department and Electrical and Electronic Engineering, University of Technology, Sydney, Australia (Dillon, T.S.) |
| Abstract | Fuzzy regression is a commonly used approach for modeling 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 modeling of epoxy dispensing process is carried out. |
| Starting Page | 2261 |
| Ending Page | 2267 |
| File Size | 193332 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424473151 |
| ISSN | 10987584 |
| e-ISBN | 9781424473175 |
| e-ISBN | 9781424473168 |
| DOI | 10.1109/FUZZY.2011.6007322 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Manufacturing processes Data models Encapsulation Integrated circuit modeling Linear regression Genetic programming manufacturing process modeling fuzzy regression |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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