An Adaptive Recursive Least Square Algorithm for Feed Forward Neural Network and Its Application

Abstract

In high dimension data fitting, it is difficult task to insert new training samples and remove old-fashioned samples for feed forward neural network (FFNN). This paper, therefore, studies dynamical learning algorithms with adaptive recursive regression (AR) and presents an advanced adaptive recursive (AAR) least square algorithm. This algorithm can efficiently handle new samples inserting and old samples removing. This AAR algorithm is applied to train FFNN and makes FFNN be capable of simultaneously implementing three processes of new samples dynamical learning, old-fashioned samples removing and neural network (NN) synchronization computing. It efficiently solves the problem of dynamically training of FFNN. This FFNN algorithm is carried out to compute residual oil distribution.