Performance prediction of a parallel Monte Carlo application: a neural network approach
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
A neural network based methodology for performance evaluation of parallel systems
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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For some classes of nonlinear systems or time series, an operating point dependent NARMA model can be used to present the system. In this paper, we attempt to design artificial neural networks that can help in the automatic identification and prediction of such model. For this purpose, we use the Extended Sample Autocorrelation Function (ESACF) as a feature extractor for the network identification and the robust ANN filter for the robust prediction. The network is tested via different noise level in the identification and prediction process to show the accuracy of the connectionist approach and its robust estimation.