Subband adaptive filtering algorithm over functional link neural network

In this paper, a subband adaptive filtering algorithm is developed over functional link neural network (FLNN) in order to overcome the issue of slow convergence of FLNNs for colored input signals. The basic idea is to introduce a delayless multi-sampled multiband-structured subband FLNN (DMSFLNN). In the proposed DMSFLNN, the principle of minimum disturbance is adopted in every subband with a view to improving the learning capacity of FLNNs. An investigation is made into the mean property of the subband adaptive filtering algorithm, thus establishing a stability condition of the DMSFLNN. Finally, Monte-Carlo simulation study is undertaken to verify the effectiveness of the proposed subband adaptive filtering algorithm