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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.<\/jats:p>","DOI":"10.1155\/2019\/9254315","type":"journal-article","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T19:31:06Z","timestamp":1568143866000},"page":"1-10","source":"Crossref","is-referenced-by-count":2,"title":["Synthetic Sensor Array Training Sets for Neural Networks"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6590-0059","authenticated-orcid":true,"given":"Oded","family":"Medina","sequence":"first","affiliation":[{"name":"Engineering Faculty, Ariel University, Ariel, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roi","family":"Yozevitch","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Ariel University, Ariel, 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