Robust total least squares by the nonlinear MCA EXIN neuron

Abstract

The robust version of the MCA EXIN linear neuron is introduced in order to solve typical minor component problems as the total least squares fitting in presence of impulsive and colored noise environments or in presence of outliers, i.e. in nonoptimal conditions for the traditional approaches. Furthermore, an analysis of the divergence of the robust neurons is made. The simulations show the better features of the NMCA EXIN neuron w.r.t. the existing nonneural and neural approaches, even in the case of high Gaussian noise together with strong outliers. This allows the use of this neuron for some very difficult problems, like in computer vision, just giving the possibility of massively high parallel architectures.