Abstract: Simulation output clearly depends on the form of the input distributions used to drive the model. Often these input distributions are fitted using finite samples of real-world data. The ...
Abstract: “Input uncertainty” refers to the (often unmeasured) effect of not knowing the true, correct distributions of the basic stochastic processes that drive the simulation. These include, for ...
During the recent decade, deep learning technology, particularly deep neural network (DNN), has gained tremendous popularity in various fields including signal processing (SP). As a data-driven ...
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