{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T08:05:31Z","timestamp":1769069131799,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,22]],"date-time":"2020-03-22T00:00:00Z","timestamp":1584835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.","award":["Research Program for Agricultural Science & Technology Development (Project No. PJ01389103)"],"award-info":[{"award-number":["Research Program for Agricultural Science & Technology Development (Project No. PJ01389103)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R2 of 0.94). In addition, the method using an algorithm based on an OFNN was proposed for optimizing considered six window-opening behavior. Three case studies confirmed the optimization performance of OFNN in the nonlinear model and verified the performance through simulations. Finally, a control system based on this logic was used in a field experiment for six days by comparing two greenhouses driven by conventional control logic and the developed control logic; a comparison of the results showed RMSEs of 3.01 \u00b0C and 2.45 \u00b0C, respectively. It confirmed the improved control performance in comparison to a conventional ventilation control system.<\/jats:p>","DOI":"10.3390\/s20061756","type":"journal-article","created":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T07:16:08Z","timestamp":1585034168000},"page":"1756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control"],"prefix":"10.3390","volume":"20","author":[{"given":"Dae-Hyun","family":"Jung","sequence":"first","affiliation":[{"name":"Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung-si, Gangwon-do 25451, Korea"},{"name":"Department of Biosystems and Biomaterial Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea"}]},{"given":"Hak-Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biosystems and Biomaterial Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea"}]},{"given":"Joon Yong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biosystems and Biomaterial Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea"}]},{"given":"Taek Sung","family":"Lee","sequence":"additional","affiliation":[{"name":"Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung-si, Gangwon-do 25451, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5846-4233","authenticated-orcid":false,"given":"Soo Hyun","family":"Park","sequence":"additional","affiliation":[{"name":"Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung-si, Gangwon-do 25451, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1080\/14620316.2015.11668717","article-title":"Response of tomato crop growth and development to a vertical temperature gradient in a semi-closed greenhouse","volume":"90","author":"Qian","year":"2015","journal-title":"J. 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