Learning automata: an introduction
Learning automata: an introduction
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Parallel batch pattern BP training algorithm of recurrent neural network
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
Parallel training of artificial neural networks using multithreaded and multicore CPUs
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
| Hi-index | 0.00 |
Neural networks have proved to be effective in solving a wide range of problems. As problems become more and more demanding, they require larger neural networks, and the time used for learning is consequently greater. Parallel implementations of learning algorithms are therefore vital for a useful application. Implementation, however, strongly depends on the features of the learning algorithm and the underlying hardware architecture. For this experimental work a dynamic problem was chosen which implicates the use of recurrent neural networks and a learning algorithm based on the paradigm of learning automata. Two parallel implementations of the algorithm were applied - one on a computing cluster using MPI and OpenMP libraries and one on a graphics processing unit using the CUDA library. The performance of both parallel implementations justifies the development of parallel algorithms.