Abstract
From the viewpoint of stochastic/statistical inverse problem, identification of transfer functions in feedback systems has been examined and applied to EEG/MEG analysis [1], since some transfer functions between output variables are invariant in a stochastic feedback system. However, there still remains an open problem of our method in scalar times transformations of output variables similar to filtering effect on transfer functions for pre-processing. From the viewpoint of normalization of data the minimum phase property of identified models is discussed to obtain transfer functions correctly. Scaling effects on transfer functions are examined in simulation study, and it is useful for diagnosis of feedback systems.
