A novel randomized recurrent artificial neural network approach: recurrent random vector functional link network

Abstract

The random vector functional link (RVFL) has successfully been employed in many applications since 1989. RVFL has a single hidden layer feedforward structure that also has direct links between the input layer and the output layer. Although nonlinearity, high generalization capacity, and fast training ability can be provided in RVFL, it can be found from the literature that higher nonlinearity can be obtained by adding recurrent feedback to an artificial neural network. In this paper, the recurrent type of RVFL (R-RVFL), which has both outer feedbacks and also inner feedbacks, is proposed. In order to evaluate and validate the proposed approach, a total of 109 public datasets were employed. Obtained results showed that R-RVFL can be employed successfully in terms of obtained success rates.