Abstract
This work presents real world examples of using different Soft Computing methods in both industrial and biological processes from the years 2005 to 2009. Multi-Objective Algorithm, Least Squares Support Vector Machine and Fuzzy Inference were applied in steel industry processes, while Decision Tree, Recursive Feature Elimination and Genetic Programming were evaluated in biological processes. Soft Computing methods were capable to predict quantities, recognize patterns and select relevant attributes in order to improve each process. This paper shows the growing development on Soft Computing and the integration of process knowledge points to a direction of increasing possibilities to achieve better performances in industrial and biological processes.
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de Carvalho, B.P.R., de Araújo, L.C. (2010). New Trends of Soft Computing Methods for Industrial and Biological Processes. In: Gao, XZ., Gaspar-Cunha, A., Köppen, M., Schaefer, G., Wang, J. (eds) Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11282-9_30
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DOI: https://doi.org/10.1007/978-3-642-11282-9_30
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