An Experimental Comparison of Recurrent Neural Networks
Bill G. Horne, Clyde Lee Giles · 1995
Many different discrete--time recurrent neural network architectures have been proposed. However, there has been virtually no effort to compare these architectures experimentally. In this paper we review and categorize many of these architectures and compare how they perform on various classes of simple problems including grammatical inference and nonlinear system identification. 1 Introduction In the past few years several recurrent neural network architectures have emerged. In this paper we categorize various discrete--time recurrent neural network architectures, and perform a quantitative comparison of these architectures on two problems: grammatical inference and nonlinear system identification. 2 RNN Architectures We broadly divide these networks into two groups depending on whether or not the states of the network are guaranteed to be observable. A network with observable Also with UMIACS, University of Maryland, College Park, MD 20742 y Published in Neural Information Pr...