Speeding up a learning algorithm for multilayer perceptrons using the MAPS Environment
Helder A. Daniel, Antonio E Ruano · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2004
Artificial neural networks, as non-linear adaptive elements, have been proposed for applications in adaptive control. Their ability to accurately approximate large classes of non-linear functions made them also a valuable tool for non-linear systems identification. However, in some cases, the parameter estimation phase may take considerable amount of time, and this is crucial in real-time applications. One way of speeding up these learning algorithms consists in executing them over a multiprocessor system. In this paper an implementation over MAPS integrated development environment, which automatically generates a parallel application from a sequential description of a learning algorithm for multilayer perceptrons is presented.