Annealing Robust Walsh Function Networks for Modeling with Outliers and Digital Implementation
Jin-Tsong Jeng, Chen‐Chia Chuang · 2005
In this paper, an annealing robust Walsh function network (ARWFN) is proposed for modeling with outliers and its digital implementation. First, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the ARWFN, and applied to adjust the weights of ARWFNs. That is, an ARLA is proposed to overcome the problems of initialization and the cut-off points in the robust learning algorithm and deal with the model with noise and outliers. It turns out that the ARWFNs with ARLA present a fast convergence speed and are robust against outliers. Second, after the learning results, the ARWFNs are easy to implement using digital circuits. Simulation results are provided to show the validity and applicability of the proposed ARWFNs.