Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks
Jarosław Bilski, Jacek Smoląg · IEEE Transactions on Parallel and Distributed Systems · 2014
A major problem encountered by researchers of dynamic neural networks is the computational complexity increasing the learning time. In this paper the parallel realization of the RTRN and the Elman networks are discussed. Both networks are examples of dynamic neural networks. Inherent parallelism of dynamic neural networks has been employed to accelerate the learning process. The proposed solution is based on a highly parallel three dimensional architecture to speed up the learning performance. The presented structures are suitable for efficient parallel realization in digital hardware or vector processors.