An experimental comparison of recurrent neural networks

Bill G. Horne, C. Lee Giles

Abstract: Many different discrete-time recurrent neural network architec(cid:173) tures have been proposed. However, there has been virtually no effort to compare these arch:tectures 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.