Title :
A genetic algorithm for training recurrent neural networks
Author :
Petridis, V. ; Kazaplis, S. ; Papaikonomou, A.
Author_Institution :
Dept. of Electr. Eng., Thessaloniki Univ., Greece
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 :
learning (artificial intelligence); limit cycles; pattern recognition; recurrent neural nets; sequences; hybrid genetic algorithm; recurrent neural networks; sequence recognition; stable limit cycle; temporal sequences; Artificial neural networks; Genetic algorithms; Genetic engineering; Limit-cycles; Network topology; Neural networks; Neurons; Recurrent neural networks; Robustness; Testing;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.714282
