NONLINEARITY IDENTIFIED BY NEURAL-NETWORK MODELS IN P-CO2 CONTROL-SYSTEM IN HUMANS

The nonlinearity included in the P-CO2 control system in humans is eva luated using the degree of nonlinearity based on a difference of resid uals, An autoregressive moving average (ARMA) model and neural network s (linear and nonlinear) are employed to model the system, and three t ypes 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 e valuation of the nonlinearity. The results of the evaluation indicate that the linear assumption for the P-CO2 control system is invalid for three subjects out of seven, In particular, strong nonlinearity was o bserved for two subjects.