Machine learning model training using de-noised data and model prediction with noise correction

Abstract

A test and measurement system has one or more inputs connectable to a device under test (DUT), and one or more processors configured to execute code that causes the one or more processors to: gather a set of training waveforms by acquiring one or more waveforms from one or more DUTs or from simulated waveforms, remove noise from the set of training waveforms to produce a set of noiseless training waveforms, and use the set of noiseless training waveforms as a training set to train a neural network to predict a measurement value for a DUT, producing a trained neural network. A method of training a neural network having receiving one or more waveforms from one or more DUTs, or generating one or more waveforms from a waveform simulator, removing noise from a set of training waveforms gathered from the one or more waveforms to produce a set of noiseless training waveforms, and use the set of noiseless training waveforms as a training set to train a neural network to predict a measurement value for a DUT, producing a trained neural network.

Dependencies are inferred from the claim text.