Gray Box Model with an SVM to Represent the Influence of PaCO2 on the Cerebral Blood Flow Autoregulation
Since the appearance of methods based on machine learning, they have been presented as an alternative to classical phenomenological modeling and there are few initiatives that attempt to integrate them. This paper presents a hybrid paradigm called gray box that blends a phenomenological description (differential equation) and a Support Vector Machine (SVM) to model a relevant problem in the field of cerebral hemodynamic. The results show that with this type of paradigm it is possible to exceed the results obtained with phenomenological models and also with the models based on learning, in addition to contributing to the description of the modelled phenomenon.
KeywordsGray Box Model Support Vector Machine Cerebral hemodynamic PaCO2
- 3.Anderson, J., McAvoy, T., Hao, O.: Use of Hybrid Models in Wastewater Systems. Ind. Eng. Chem. Res. 39, 94–1704 (2000)Google Scholar
- 5.Acuña, G., Cubillos, F., Thibault, J., Latrille, E.: Comparison of Methods for Training Grey- box Neural Models. Computers. Chem. Engng. 23, S561–S564 (1999)Google Scholar
- 8.Poulin, M., Liang, P., Robbins, P.: Dynamics of the cerebral blood flow response to step changes in end-tidal PCO2 and PO2 in humans. Journal of Applied Physiology 81, 1084–1095 (1996)Google Scholar
- 12.Schölkopf, B., Smola, A., Williamson, R., Bartlett, P.: New support vector algorithms. Neural Computation 2, 1083–1121 (1998)Google Scholar