Title :
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
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;
Conference_Titel :
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location :
Tokyo
Print_ISBN :
0-7803-5731-0
DOI :
10.1109/ICSMC.1999.812567
