Title :
An optimal two stage identification algorithm for Hammerstein-Wiener nonlinear systems
Author_Institution :
Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA
Abstract :
An optimal two stage identification algorithm is presented for Hammerstein-Wiener systems where two static nonlinear elements surround a linear block. The proposed algorithm consists of two steps: The first one is the recursive least squares and the second one is the singular value decomposition of two matrices whose dimensions are fixed and do not increase as the number of the data point increases. Moreover, the algorithm is shown to be convergent in the absence of noise and convergent with probability one in the presence of white noise
Keywords :
convergence of numerical methods; least squares approximations; nonlinear systems; optimisation; recursive estimation; singular value decomposition; white noise; Hammerstein-Wiener nonlinear systems; SVD; convergence; linear block; matrices; optimal two stage identification algorithm; recursive least squares; singular value decomposition; static nonlinear elements; white noise; Cities and towns; Ear; Least squares approximation; Least squares methods; Matrix decomposition; Nonlinear dynamical systems; Nonlinear systems; Singular value decomposition; Vectors; White noise;
Conference_Titel :
American Control Conference, 1998. Proceedings of the 1998
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-4530-4
DOI :
10.1109/ACC.1998.688354
