Recurrent neural network

From Wikipedia, the free encyclopedia

A recurrent neural network is a neural network where the connections between the units form a directed cycle. Recurrent neural networks must be approached differently from feedforward neural networks, both when analysing their behavior and training them. Recurrent neural networks can also behave chaotically. Usually, dynamical systems theory is used to model and analyse them. A popular type of recurrent neural network is the Elman network.

[edit] See also

• Hopfield network

[edit] References

• Abdi, H. "A neural network primer. Journal of Biological Systems, 2, 247-281, (1994)".
• Abdi, H. "[1] (2003). Neural Networks. In M. Lewis-Beck, A. Bryman, T. Futing (Eds): Encyclopedia for research methods for the social sciences. Thousand Oaks (CA): Sage. pp. 792-795.]".
• Abdi, H. "[2] (2001). Linear algebra for neural networks. In N.J. Smelser, P.B. Baltes (Eds.): International Encyclopedia of the Social and Behavioral Sciences. Oxford (UK): Elsevier.]".
• Abdi, H., Valentin, D., Edelman, B.E. (1999). Neural Networks. Thousand Oaks: Sage.
• Mandic, D. & Chambers, J. (2001). Recurrent Neural Networks. Wiley. 
• Elman, J.L. (1990). "Finding Structure in Time". Cognitive Science 14: 179-211.