Conclusions

Summary

A broad selection of adaptive finite impulse response (FIR) filter algorithms was examined to assess their theoretical convergence performance and computational requirements. From this examination a classification system has been specified in which the available algorithms are grouped into three classes according to convergence performance and computational complexity. These three classes are: (i) stochastic gradient (SG) algorithms, (ii) self-orthogonalising (SO) algorithms and (iii) recursive least squares (RLS) algorithms. Formerly classes (ii) and (iii) had been grouped together. Movement from class (i) through (ii) to (iii) improves convergence performance at the expense of increasing computational complexity.