Abstract
Convergence and tracking properties are discussed for a family of adaptive signal processing algorithms. The family treated includes bounded fixed gain versions of both the RM and KW stochastic approximation procedures. New results are presented that are applicable when the training data is correlated and the “optimal” solution is not unique and/or time-varying. The non-uniqueness is of interest in establishing “convergence” for constrained algorithms as well as determining “single-mode behavior”.
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