Integration of design of experiment techniques and intelligent systems for modelling and prediction
| dc.contributor.author | Kho, Hiaw San | |
| dc.date.accessioned | 2014-10-27T06:22:24Z | |
| dc.date.available | 2014-10-27T06:22:24Z | |
| dc.date.issued | 2009 | |
| dc.description | Master | en_US |
| dc.description.abstract | In this thesis, hybrid models comprising Design of Experiment (DOE) techniques and intelligent systems for tackling data-based modelling and prediction problems are presented. Traditionally, DOE techniques employ statistical regression methods to study the relationship between the explanatory variables and response variables in observational studies, and an empirical model is then built for prediction purposes. In this research, the fuzzy linear regression (FLR) approach is proposed as an alternative for model building in DOE whereby statistical regression methods are inappropriate. The usefulness of the FLR model is first evaluated using two benchmarks, i.e. the injection moulding process and wire electrical discharge machining (WEDM) process, and the performance is evaluated using the R2 value. The results show that FLR is able to improve R2 by at least 27.13% as compared with those from statistical regression methods. However, the limitation of FLR is that a lot of parameters need to be pre-set empirically in order to achieve good results. Thus, the FLR is integrated with the GA (Genetic Algorithm) to produce a novel FLR-GA model. The GA is used to facilitate parameter tuning in FLR and to improve its robustness and prediction capabilities. The effectiveness of the proposed FLR-GA model is demonstrated using another benchmark problem, i.e., the epoxy dispensing process, in addition to the injection moulding and WEDM processes. As compared with FLR without GA, FLR-GA is able to improve the R2 values by 39.33% (injection moulding), 24.21% (WEDM process), and to reduce the prediction error percentage from 6.24% to 0.24% (epoxy dispensing process). Besides benchmark studies, two real modelling and prediction tasks, i.e., a stereo vision system and a soda pulping process, are studied. Performance comparisons between FLR-GA and LSR (Least Square Regression) are conducted, the improvement achieved is 4.77% and 35.87% respectively. These results positively demonstrate the potentials of FLR-GA as an effective model building tool in DOE techniques for undertaking data-based modelling and prediction tasks. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/166 | |
| dc.language.iso | en | en_US |
| dc.subject | Science Physic | en_US |
| dc.subject | Design | en_US |
| dc.subject | Intelligent systems | en_US |
| dc.title | Integration of design of experiment techniques and intelligent systems for modelling and prediction | en_US |
| dc.type | Thesis | en_US |
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