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AIAA 2020-1869
Session: Modeling and Simulation for Guidance and Navigation
Published Online:

We discuss a novel method to train a neural network from noisy data, using Optimal Transport based filtering. We show a comparative study of this methodology with three other filters: the Extended Kalman filter, the Ensemble Kalman filter, and the Unscented Kalman filter, that can also be used for the purpose of training a neural network. We empirically establish that Optimal Transport based filter performs better than the other three filters with respect to root mean square error measure, for non-Gaussian noise in the output. We demonstrate the efficacy of utilizing the Optimal Transport based filtering for neural network training in the context of predicting Mackey-Glass chaotic time series data.