Functional Link Artificial Neural Networks Filter for Gaussian Noise

Abstract

In this paper, FLANN(functional link ANN) filter is presented for Gaussian noise. FLANN is a singer layer with expanded input vectors and has lower computational cost than MLP(multilayer perceptron). Three types of functional expansion are discussed. BP(back propagation algorithm) for nonlinear activation function and matrix calculation for identical activation function are exploited for training FLANN. Simulation shows that convergence is not guaranteed in BP and related to the initial weight matrix and training images, and that linear FLANN trained by matrix calculation performs better than both nonlinear FLANN trained by BP and Wiener filter in detail region in environment of Gaussian noise