Title :
Invariance principles and applications to distributed parameter identification
Author :
Yin, G. ; Fitzpatrick, B.G.
Author_Institution :
Dept. of Math., Wayne State Univ., Detroit, MI, USA
Abstract :
A nonlinear least squares parameter estimation procedure is discussed. The main objective is to extend previous results in order to obtain certain functional invariance theorems. In particular, weak convergence methods are used to prove an asymptotic normality result and Strassen´s invariance principle is applied to establish a law of the iterated logarithm. Some examples are presented
Keywords :
convergence; invariance; least squares approximations; parameter estimation; Strassen´s invariance principle; asymptotic normality; distributed parameter identification; functional invariance theorems; nonlinear least squares parameter estimation; weak convergence methods; Accelerometers; Brain modeling; Convergence; Damping; Design for experiments; Least squares approximation; Least squares methods; Mathematics; Parameter estimation; Testing;
Conference_Titel :
Decision and Control, 1990., Proceedings of the 29th IEEE Conference on
Conference_Location :
Honolulu, HI
DOI :
10.1109/CDC.1990.203486
