Table of Contents
Information, Volume 11, Issue 10 (October 2020) – 36 articles
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Cover Story (view full-size image) We investigated the ability of deep learning neural networks to provide mapping between the [...] Read more. We investigated the ability of deep learning neural networks to provide mapping between the features of a parallel distributed discrete-event simulation (PDDES) system (software and hardware) to a time synchronization scheme to optimize speed-up performance. Deep belief networks (DBNs) were used, which due to their multiple layers with feature detectors at the lower layers and a supervised scheme at the higher layers, can provide nonlinear mapping. The mapping mechanism works by considering simulation constructs, hardware, and software intricacies such as simulation objects, concurrency, iterations, routines, and messaging rates with an importance level based on a cognitive approach. The result is a synchronization scheme with breathing time buckets, breathing time warp, and time warp to optimize the speed-up. View this paper.