DocumentCode :
2755032
Title :
Transient dynamics of non-linear models describing multi-stable stochastic systems
Author :
Rajala, Miika ; Ritala, Risto
Author_Institution :
Inst. of Meas. & Inf. Technol., Tampere Univ. of Technol., Finland
Volume :
5
fYear :
2005
fDate :
31 July-4 Aug. 2005
Firstpage :
3324
Abstract :
Multi-stable stochastic systems are inherently nonlinear and typically have long transient times before stationary state is reached. Black-box dynamic models always assume the data to be from a stationary system. In this paper, we discuss a method to assess whether data is long enough to justify non-linear time series model. Our method discretizes state space and studies the corresponding Markov chain. The eigenvalues of the Markov transition operator provide leading transient time constants. The method is illustrated by generating data with a known multi-stable model and then analyzing the data with NNAR(1) and polynomial lag-1 time series model.
Keywords :
Markov processes; eigenvalues and eigenfunctions; nonlinear control systems; stability; stochastic systems; time series; Markov chain; Markov transition operator; black-box dynamic model; eigenvalues; multistable stochastic system; nonlinear model; nonlinear time series; polynomial time series; transient dynamics; Data analysis; Data models; Eigenvalues and eigenfunctions; Indium tin oxide; Information technology; Polynomials; State-space methods; Stochastic processes; Stochastic systems; Transient analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
Type :
conf
DOI :
10.1109/IJCNN.2005.1556461
Filename :
1556461
Link To Document :
بازگشت