System identification of a circadian signal source using BP neural networks

Author

Cisse, Y. ; Kinouchi, Y. ; Nagashino, H. ; Akutagawa, M.

Author_Institution

Dept. of Electr. & Electron. Eng., Tokushima Univ., Japan

Volume

6

fYear

1998

fDate

29 Oct-1 Nov 1998

Firstpage

3032

Abstract

The dynamics of some biological signal sources may be identified through measured data. The regulating characteristics of the wake-sleep circadian rhythm is identified here as an example by using BP neural networks. As a result, a MA model of neural networks can acquire the characteristics. The change of wake-sleep period is almost controlled suppressively by the data of preceding three days. The change of the dynamics can be evaluated by internal representation of the network. This method may be useful for medical diagnoses

Keywords

backpropagation; biocontrol; identification; moving average processes; neural nets; neurophysiology; nonlinear dynamical systems; physiological models; sleep; autonomous oscillator; backpropagation neural networks; biological signal source dynamics; circadian signal source; internal representation; medical diagnoses; nonlinear moving average model; regulating characteristics; system identification; vector representation; wake-sleep circadian rhythm; Biological neural networks; Circadian rhythm; Fluctuations; Medical diagnosis; Neural networks; Neurons; Oscillators; Signal processing; System identification; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE

Conference_Location

Hong Kong

ISSN

1094-687X

Print_ISBN

0-7803-5164-9

Type

conf

DOI

10.1109/IEMBS.1998.746130

Filename

746130