A new incremental affine projection-based adaptive algorithm for distributed networks

摘要

A new incremental adaptive learning scheme based on the affine projection algorithm (APA), which is developed from Newton's method, is formulated for distributed networks to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The simulation results verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance at the steady-state stage with lower computational cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, and memory cost than a recursive-least-squares (RLS)-based method.