Fast tracking conjugate gradient algorithm

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.