A stochastic model for a pseudo affine projection algorithm operating in a nonstationary environment

This paper presents a statistical analysis of a pseudo affine projection (PAP) algorithm, obtained from the affine projection algorithm (AP) for a step size /spl alpha/<1 and a scalar error signal in the weight update. Deterministic recursive equations are derived for the mean weight and for the mean square error for a large number of adaptive taps N compared to the order P of the algorithm. Simulations are presented which show excellent agreement with the theory in the transient and steady states. The PAP learning behavior is of special interest in applications where tradeoffs are necessary between convergence speed and steady-state misadjustment.

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