A genetic algorithm for training recurrent neural networks
- 24 August 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3, 2706-2709
- https://doi.org/10.1109/ijcnn.1993.714282
Abstract
A hybrid genetic algorithm is proposed far training neural networks with recurrent connections. A fully connected recurrent ANN model is employed and tested over a number of problems. Simulation results are presented for three problems: generation of a stable limit cycle, sequence recognition and storage and reproduction of temporal sequences.Keywords
This publication has 7 references indexed in Scilit:
- A hybrid genetic algorithm for training neural networksPublished by Elsevier ,1992
- Storing temporal sequencesNeural Networks, 1991
- A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksNeural Computation, 1989
- Learning State Space Trajectories in Recurrent Neural NetworksNeural Computation, 1989
- Backpropagation in Perceptrons with FeedbackPublished by Springer Nature ,1989
- Generalization of back-propagation to recurrent neural networksPhysical Review Letters, 1987
- Outline for a Logical Theory of Adaptive SystemsJournal of the ACM, 1962