{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:28:35Z","timestamp":1760171315130,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>In recent years, deep learning has been applied to build soft sensor models. Compared to the classical latent variable models, the model based on deep learning has a good performance in tackling nonlinearity in process data. However, static soft sensor based on deep neural network (SSSDNN) fails to take into account the dynamic characteristics, which are unavoidable in some applications. To improve the performance of the soft sensor, a novel dynamic soft sensor model based on impulse response template and deep neural network (DSSDNN) is proposed by means of Wiener structure, and an iterative algorithm will be used to train this novel model. A case study based on real debutanizer column data demonstrates the desirable prediction performance of DSSDNN and shows that the DSSDNN is able to obtain better approximation accuracy than the static soft sensor model in nonlinear processes with dynamic characteristics.<\/jats:p>","DOI":"10.3233\/978-1-61499-927-0-593","type":"book-chapter","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:06:08Z","timestamp":1740053168000},"source":"Crossref","is-referenced-by-count":1,"title":["Dynamic Soft Sensor Based on Impulse Response Template and Deep Neural Network for Industrial Processes"],"prefix":"10.3233","author":[{"family":"Liu Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Wang Kangcheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Shang Chao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Yang Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Lyu Wenxiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Huang Dexian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IV"],"original-title":[],"deposited":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:25:55Z","timestamp":1740054355000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-926-3&spage=593&doi=10.3233\/978-1-61499-927-0-593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-927-0-593","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}