Title :
On reduced order identification; revisiting `On some system identification techniques for adaptive filtering´
Author :
Fan, Hong ; Nayeri, Majid
Author_Institution :
Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA
fDate :
9/1/1990 12:00:00 AM
Abstract :
T. Soderstrom and P. Stoica (1988) have previously discussed three algorithms: the Steiglitz-McBride method (SMM), the recursive gradient method (RGM), and the instrumental variable method (IVM). These adaptive algorithms are revisited. Their stability and convergence points are studied and discussed under a reduced-order framework which is more realistic in a practical situation. It is concluded that no algorithm has compelling advantages over the others; each one has its own advantages and disadvantages. One is faced with a tradeoff in choosing one against the others in a specific application
Keywords :
adaptive filters; filtering and prediction theory; identification; Steiglitz-McBride method; adaptive algorithms; adaptive filtering; instrumental variable method; recursive gradient method; reduced order identification; reduced-order framework; stability; system identification techniques; Adaptive filters; Adaptive signal processing; Circuits; Lattices; Least squares approximation; Recursive estimation; Signal processing; Signal processing algorithms; Speech processing; System identification;
Journal_Title :
Circuits and Systems, IEEE Transactions on
