{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:43:31Z","timestamp":1780501411238,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T00:00:00Z","timestamp":1608768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the process of the \u201csmart\u201d house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a \u201csmart\u201d house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.<\/jats:p>","DOI":"10.3390\/s21010047","type":"journal-article","created":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T09:02:44Z","timestamp":1608800564000},"page":"47","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems"],"prefix":"10.3390","volume":"21","author":[{"given":"Vasyl","family":"Teslyuk","sequence":"first","affiliation":[{"name":"Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6883-0233","authenticated-orcid":false,"given":"Artem","family":"Kazarian","sequence":"additional","affiliation":[{"name":"Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3678-9229","authenticated-orcid":false,"given":"Natalia","family":"Kryvinska","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Management, Comenius University in Bratislava, Bratislava 25 82005, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan","family":"Tsmots","sequence":"additional","affiliation":[{"name":"Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1016\/j.jclepro.2016.10.006","article-title":"A review of Internet of Things for smart home: Challenges and solutions","volume":"140","author":"Stojkoska","year":"2017","journal-title":"J. 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