Convergence Results for Adaptive Filtering with Correlated Data.
COLORADO STATE UNIV FORT COLLINS DEPT OF ELECTRICAL ENGINEERING
Pagination or Media Count:
New almost sure convergence results for a special form of the multidimensional Robbins-Monro stochastic approximation procedure are given. The results are applicable to cases where the training data is heavily correlated. No conditional expectation properties or bounded assumptions are required to apply the new results. For example, when the data sequence is normal and 1 M-dependent, 2 autoregressive moving average ARMA, or 3 band-limited, the results can be used to establish the almost sure convergence of each algorithm treated. The special form of the Robbins-Monro procedure considered is motivated by a consideration of several algorithms that have been proposed for discrete time adaptive signal processing applications. Most of these algorithms can also be viewed as stochastic gradient-following algorithms. Several examples are presented to illustrate the applicability of the convergence results.
- Non-Radio Communications