Abstract
In this paper an algorithm is described, which is based on the Real-Time Recurrent Learning (RTRL) algorithm by Williams and Zipser
Preview
Unable to display preview. Download preview PDF.
References
R.J. Williams and D. Zipser. Experimental Analysis of the Real-Time Recurrent Learning Algorithm. Connection Science, (1), 87–111, 1989.
G. Cybenko. Approximation by Superpositions of a Sigmoidal Function. Mathematical Control Signals and Systems, (2), 303–314, 1989.
K. Funahashi and Y. Nakamura. Approximation of Dynamcal Systems by Continuous Time Recurrent Networks. Neural Networks, (6), 801–806, 1993.
B.A. Pearlmutter. Gradient Calculations for Dynamic Recurrent Neural Networks: a Survey. IEEE Transactions on Neural Networks, (6), 1212–1228, 1995.
G.V. Puskorius and L.A.Feldkamp. Neurocontrol of Nonlinear Dynamcal Systems with Kalman Filter Trained Recurrent Networks. IEEE Transactions on Neural Networks, (5), 279–297, 1994.
A.G. Parlos, K.T. Chong and A.F. Atiya. Application of the Recurrent Multilayer Perceptron in Modelling Complex Process Dynamics. IEEE Transactions on Neural Networks, (5). 255–266, 1994.
C.B. Miller and C.L.Giles. Experimental Comparison of the Effect of Order in Recurrent Neural Networks. International Journal of Pattent Recognition and Artificial Intelligence. (7), 849–872, 1993.
S. Miyoshi and K. Nakayama. Probabilistic Memory Capacity of Recurrent Neural Networks. Proc. of the IEEE International Conference on Neural Networks 96, Washington. DC., (2), 1291–1296. 1996.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meert, K., Ludik, J. (1997). A multilayer real-time, recurrent learning algorithm for improved convergence. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020195
Download citation
DOI: https://doi.org/10.1007/BFb0020195
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63631-1
Online ISBN: 978-3-540-69620-9
eBook Packages: Springer Book Archive