Abstract
In this paper we developed a Type-2 Fuzzy Logic System (T2FLS) in order to model a batch biotechnological process. Type-2 fuzzy logic systems are suitable to drive uncertainty like that arising from process measurements. The developed model is contrasted with an usual type-1 fuzzy model driven by the same uncertain data. Model development is conducted, mainly, by experimental data which is comprised by thirteen data sets obtained from different performances of the process, each data set presents a different level of uncertainty. Parameters from models are tuned with gradient-descent rule, a technique from neural networks field. © 2011 Springer-Verlag.
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CITATION STYLE
Hernández Torres, P., Espejel Rivera, M. A., Ramos Velasco, L. E., Ramos Fernández, J. C., & Waissman Vilanova, J. (2011). Type-2 neuro-fuzzy modeling for a batch biotechnological process. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7095 LNAI, pp. 37–45). https://doi.org/10.1007/978-3-642-25330-0_4
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