ADAPTIVE IIR FILTERING ALGORITHMS FOR SYSTEM-IDENTIFICATION - A GENERAL FRAMEWORK

Adaptive IIR (infinite impulse response) filters are particularly bene ficial in modeling real systems because they require lower computation al complexity and can model sharp resonances more efficiently as compa red to the FIR (finite impulse response) counterparts. Unfortunately, a number of drawbacks are associated with adaptive IIR filtering algor ithms that have prevented their widespread use, such as: Convergence t o biased or local minimum solutions, requirement of stability monitori ng, and slow convergence. Most of the recent research effort on this f ield is aimed at overcoming some of the above mentioned drawbacks. In this paper, a number of known adaptive IIR filtering algorithms are pr esented using a unifying framework that is useful to interrelate the a lgorithms and to derive their properties. Special attention is given t o issues such as the motivation to derive each algorithm and the prope rties of the solution after convergence. Several computer simulations are included in order to verify the predicted performance of the algor ithms.