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Operations Research, Systems Engineering and Industrial Engineering
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Full-Text Articles in Engineering
Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller
Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller
Theses and Dissertations
Using convolutional neural networks (CNNs) for image classification for each frame in a video is a very common technique. Unfortunately, CNNs are very brittle and have a tendency to be over confident in their predictions. This can lead to what we will refer to as “flickering,” which is when the predictions between frames jump back and forth between classes. In this paper, new methods are proposed to combat these shortcomings. This paper utilizes a Bayesian CNN which allows for a distribution of outputs on each data point instead of just a point estimate. These distributions are then smoothed over multiple …
Characterizing Complex-Valued Neural Network Model Approximations Of 4-Input 4-Output Complex-Valued Reference Block Models, Larry C. Llewellyn Ii
Characterizing Complex-Valued Neural Network Model Approximations Of 4-Input 4-Output Complex-Valued Reference Block Models, Larry C. Llewellyn Ii
Theses and Dissertations
System simulation models are often decomposed and abstracted as a collection of interconnected subsystem block models to facilitate system understanding, design, and analysis. Each subsystem block model contains mathematical functions that receive, process, and transmit signals that are modeled as real numbers, complex numbers, and/or logic values. This dissertation evaluates the use of a single two-layer complex-valued neural network model to approximate 4-input, 4-output subsystem reference block models in terms of accuracy, performance, and error. The research is novel in that it uses a neural network for continuous function approximation instead of data categorization; it uses a neural network designed …