Towards Improving the Applicability of Non-parametric Multiple Comparisons to Select the Best Soft Computing Models in Rubber Extrusion Industry
In this paper we propose different strategies to apply non-parametric multiple comparisons in industrial environments. These techniques have been widely used in theoretical studies and research to evaluate the performance of models, but they are still far from being implemented in real applications. So, we develop three new automatized strategies to ease the selection of soft computing models using data from industrial processes. A rubber products manufacturer was selected as a real industry to conduct the experiments. More specifically, we focus our study on the mixing phase. The rheology curve of rubber compounds is predicted to anticipate possible failures in the vulcanization process. More accurate predictions are needed to provide set points to enhance the control the process, particularly working in this rapidly changing environment. Selecting among a wide range of models increases the probability of achieving the best predictions. The main goal of our methodology is therefore to automatize the selection process when many choices are availables. The models based on soft computing used to validate our proposal are neural networks and support vector machines and also other alternatives such as linear and rule-based models.
KeywordsSupport Vector Machine Multilayer Perceptron Non-parametric comparison Friedman Rubber Mixing Process
Unable to display preview. Download preview PDF.
- 8.Quinlan: Combining instance-based and model-based learning. In: Proceedings of the Tenth International Conference on Machine Learning, pp. 236–243 (1993)Google Scholar
- 10.Wilkinson, G.N., Rogers, C.E.: Symbolic description of factorial models for analysis of variance. Journal of the Royal Statistical Society. Series C (Applied Statistics) 22, 392–399 (1973)Google Scholar