Type-2 neuro-fuzzy modeling for a batch biotechnological process

Authors:
Pablo Hernández Torres;María Angélica Espejel Rivera;Luis Enrique Ramos Velasco;Julio Cesar Ramos Fernández;Julio Waissman Vilanova
Affiliations:
Centro de Investigación en Tecnologías de Información y Sistemas, Universidad Autónoma del Estado Hidalgo, Hidalgo, México;Universidad la Salle Pachuca, Pachuca, Hidalgo, México;Centro de Investigación en Tecnologías de Información y Sistemas, Universidad Autónoma del Estado Hidalgo, Hidalgo, México;Universidad Politécnica de Pachuca, Carretera Pachuca-Cd. Sahagún, Hidalgo, México;Universidad de Sonora, Hermosillo, Sonora, México
Venue:
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Year:
2011

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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.