Title :
Two-stage iterative estimation algorithm for systems with colored noises using the data filtering
Author_Institution :
Key Lab. of Adv. Process Control for Light Ind., Jiangnan Univ., Wuxi, China
Abstract :
By means of the data filtering technique, this presents a two-stage least squares based iterative algorithm for systems with colored noises, i.e., controlled autoregressive autoregressive moving average (CARARMA) systems. The key is to obtain two identification models by using the decomposition technique, one including the parameters of the system model, and the other including the parameters of the noise model. Then we use the least squares principle to interactively estimate the parameters of two submodels. The proposed algorithm has lower computational cost and is effective for estimating the parameters of the CARARMA systems.
Keywords :
autoregressive moving average processes; filtering theory; iterative methods; least squares approximations; parameter estimation; CARARMA systems; colored noises; computational cost; controlled autoregressive autoregressive moving average system; data filtering technique; decomposition technique; identification models; noise model parameters; submodel parameter estimation; system model parameters; two-stage iterative estimation algorithm; two-stage least squares based iterative algorithm; Computational modeling; Least squares approximation; Mathematical model; Parameter estimation; Signal processing algorithms; Stochastic processes; Vectors; Iterative identification; Least squares; Parameter estimation;
Conference_Titel :
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4577-2073-4
DOI :
10.1109/CCDC.2012.6244336
