Title :
Application of Benveniste´s convergence results in the study of adaptive IIR filtering algorithms
Author_Institution :
Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA
fDate :
7/1/1988 12:00:00 AM
Abstract :
It is shown that the weak convergence results of A. Benveniste et al. (IEEE Trans. Automat. Contr., vol.AC-25, p.1042-58, Dec. 1980) can be used to prove convergence of some adaptive infinite impulse response (IIR) filtering algorithms. The association of the algorithms with some ordinary differential equations for constant gains, which parallels the theory of L. Ljung et al. (1983), is suitable for constant-gain adaptive filtering applications. Convergence proofs for a prefiltering algorithm, for a simple constant-gain version of the recursive maximum-likelihood algorithm, and for the well-known simple hyperstable adaptive recursive filter (SHARF) algorithm are given as examples
Keywords :
adaptive filters; convergence of numerical methods; digital filters; filtering and prediction theory; adaptive IIR filtering algorithms; constant-gain adaptive filtering; convergence; infinite impulse response; prefiltering algorithm; recursive maximum-likelihood algorithm; simple hyperstable adaptive recursive filter; Adaptive filters; Algorithm design and analysis; Convergence; Differential equations; Filtering algorithms; Finite impulse response filter; IIR filters; Poles and zeros; Stochastic processes; System identification;
Journal_Title :
Information Theory, IEEE Transactions on
