Title :
Fast tracking conjugate gradient algorithm
Author :
Kim, Dai I. ; Wilde, P. De
Author_Institution :
Dept. of Electron. & Electr. Eng., Univ. Coll. London, UK
Abstract :
This paper describes a novel Conjugate Gradient (CG) algorithm utilizing a noise-immunized forgetting factor in order to boost the tracking capability for time-varying parameters. The new algorithm is based on re-initializing the forgetting factor when it encounters an unexpected parameter change and has a noise-immunity property due to the counter logic function. Fast tracking and low parametric error variance properties are verified through computer simulation in a system identification problem. In addition, the convergence property is analyzed by a Chebyshev polynomial approximation. It is shown that the convergence of the CG algorithm is speeded up by an acceleration term when compared to the Steepest Descent (SD) algorithm
Keywords :
Chebyshev approximation; adaptive filters; conjugate gradient methods; convergence of numerical methods; identification; tracking filters; Chebyshev polynomial approximation; adaptive filtering; computer simulation; convergence property; counter logic function; fast tracking conjugate gradient algorithm; low parametric error variance properties; noise-immunity property; noise-immunized forgetting factor; system identification problem; time-varying parameters; tracking capability; Acceleration; Character generation; Chebyshev approximation; Computer errors; Computer simulation; Convergence; Counting circuits; Logic functions; Polynomials; System identification;
Conference_Titel :
Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on
Conference_Location :
Geneva
Print_ISBN :
0-7803-5482-6
DOI :
10.1109/ISCAS.2000.858800
