Abstract
We present a novel affine projection algorithm which reduces complexity by selecting a subset of input regressors at every iteration. The optimal selection of input regressors is derived by comparing the cost functions based on the principle of minimum disturbance. The proposed algorithm shows good convergence performance with various experimental results
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