Title :
Parallel training of simple recurrent neural networks
Author :
McCann, Peter J. ; Kalman, Barry L.
Author_Institution :
Dept. of Comput. Sci., Washington Univ., St. Louis, MO, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
A concurrent implementation of the method of conjugate gradients for training Elman networks is discussed. The parallelism is obtained in the computation of the error gradient and the method is therefore applicable to any gradient descent training technique for this form of network. The experimental results were obtained on a Sun Sparc Center 2000 multicomputer. The Spare 2000 is a shared memory machine well suited to coarse-grained distributed computations, but the concurrency could be extended to other architectures as well
Keywords :
learning (artificial intelligence); parallel processing; recurrent neural nets; shared memory systems; Elman networks; Sun Sparc Center 2000 multicomputer; coarse-grained distributed computations; conjugate gradients; error gradient; gradient descent training; parallel training; recurrent neural networks; shared memory machine; Computer architecture; Computer errors; Computer networks; Computer science; Concurrent computing; Distributed computing; Kalman filters; Neural networks; Recurrent neural networks; Sun;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374157
