Nonlinear Finite Impulse Response Estimation using Regularized Neural Networks

Abstract

Abstract This work presents a new regularization scheme for identifying nonlinear finite impulse response (NFIR) models using artificial neural networks (ANN). Prior knowledge, such as the exponentially decaying nature of an impulse response, is included during the identification using a regularization approach inspired on the well-known regularized linear finite impulse response identification literature. More specifically the sensitivity of the modeled output with respect to the delayed input of the NFIR model is penalized to provide an exponentially decaying prior. The proposed method is illustrated and compared to other ANN regularization schemes on a simulation example.