title: Nonlinearity identified by neural network models in Pco2 control system in humans.
abstract: The nonlinearity included in the PCO2 control system in humans is evaluated using the degree of nonlinearity based on a difference of residuals. An autoregressive moving average (ARMA) model and neural networks (linear and nonlinear) are employed to model the system, and three types of network (Jordan, Elman and fully interconnected) are compared. As the Jordan-type linear network cannot approximate respiratory data accurately, the other two types and the ARMA model are used for the evaluation of the nonlinearity. The results of the evaluation indicate that the linear assumption for the PCO2 control system is invalid for three subjects out of seven. In particular, strong nonlinearity was observed for two subjects.
authors.affiliation: Division of Electronic Engineering, Tokyo Medical and Dental University, Japan. futuoka@elec.i-mde.tmd.ac.jp
authors.forename: Y
authors.initials: Y
authors.lastname: Fukuoka
authors.forename: M
authors.initials: M
authors.lastname: Noshiro
authors.forename: H
authors.initials: H
authors.lastname: Shindo
authors.forename: H
authors.initials: H
authors.lastname: Minamitani
authors.forename: M
authors.initials: M
authors.lastname: Ishikawa
mesh_terms: D000328:Adult; D002245:Carbon Dioxide / Q000502:physiology*; D005246:Feedback; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D010313:Partial Pressure; D012119:Respiration / Q000502:physiology*
chemical_list: D002245:Carbon Dioxide