A hierarchical Bayesian scheme for nonlinear dynamical system reconstruction and prediction with neural nets

Author

Matsumoto, T. ; Nakajima, Y. ; Saito, M. ; Sugi, J.

Author_Institution

Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan

Volume

4

fYear

1999

fDate

1999

Firstpage

1119

Abstract

A hierarchical Bayesian scheme with neural nets is used to reconstruct nonlinear dynamical systems. Typical examples include chaotic time series prediction and energy demand prediction of a building. The latter class of problems helps in saving energy and reduction of CO₂ emissions. A difference between these two classes of problems lies in the fact that the former gives rise to autonomous dynamical systems while the latter leads to non-autonomous dynamical systems

Keywords

Bayes methods; HVAC; chaos; forecasting theory; load forecasting; neural nets; nonlinear dynamical systems; time series; CO₂ emissions; autonomous dynamical systems; building; chaotic time series prediction; energy demand prediction; hierarchical Bayesian scheme; nonautonomous dynamical systems; nonlinear dynamical system reconstruction; Bayesian methods; Chaos; Energy consumption; Ice; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Power engineering and energy; Testing; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on

Conference_Location

Tokyo

ISSN

1062-922X

Print_ISBN

0-7803-5731-0

Type

conf

DOI

10.1109/ICSMC.1999.812567

Filename

812567