Title :
RLS estimation of input/output models for distributed systems in the presence of noise
Author :
Gibson, J.S. ; Lee, G.H. ; Wu, C.-F.
Author_Institution :
California Univ., Los Angeles, CA, USA
Abstract :
This paper discusses recursive-least-squares (RLS) estimation of parameters in digital input/output models of linear time-invariant distributed systems. The equivalence between the parameter estimation problem for infinitely long data sequences and a linear-quadratic optimal control problem on a finite interval is used to compute theoretical asymptotic values for the parameters estimated from finite data sequences. Numerical results are given for a sampled-data version of a wave equation
Keywords :
distributed parameter systems; least squares approximations; linear quadratic control; multidimensional systems; noise; recursive estimation; sampled data systems; wave equations; distributed parameter systems; infinite dimensional systems; input/output models; linear time-invariant systems; linear-quadratic optimal control; noise; parameter estimation; recursive-least-squares; sampled-data systems; wave equation; Estimation theory; Hilbert space; Lattices; Optimal control; Parameter estimation; Partial differential equations; Resonance light scattering; System identification; Transfer functions; White noise;
Conference_Titel :
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4187-2
DOI :
10.1109/CDC.1997.657759
