Synthetic Sensor Array Training Sets for Neural Networks

الملخص EN

It is often hard to relate the sensor’s electrical output to the physical scenario when a multidimensional measurement is of interest.

An artificial neural network may be a solution.

Nevertheless, if the training data set is extracted from a real experimental setup, it can become unreachable in terms of time resources.

The same issue arises when the physical measurement is expected to extend across a wide range of values.

This paper presents a novel method for overcoming the long training time in a physical experiment set up by bootstrapping a relatively small data set for generating a synthetic data set which can be used for training an artificial neural network.

Such a method can be applied to various measurement systems that yield sensor output which combines simultaneous occurrences or wide-range values of physical phenomena of interest.

We discuss to which systems our method may be applied.

We exemplify our results on three study cases: a seismic sensor array, a linear array of strain gauges, and an optical sensor array.

We present the experimental process, its results, and the resulting accuracies.