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Second, a generative knowledge-based transfer learning model is proposed with the performance advantages of the belief rule base (BRB) method on few-shot learning, which combines expert knowledge and simulated training data to obtain a generalized BRB model and then fine-tunes the generalized model with real data to obtain a dedicated BRB model. Third, through uniform sampling of NASA lithium battery data and simulating few-shot conditions, the generative transfer-belief rule base (GT-BRB) method proposed in this paper is verified to be feasible for few-shot health condition estimation and improves the estimation accuracy of the BRB method by approximately 17.3%.<\/jats:p>","DOI":"10.1007\/s40747-022-00787-6","type":"journal-article","created":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T07:03:00Z","timestamp":1660978980000},"page":"965-979","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Generative knowledge-based transfer learning for few-shot health condition estimation"],"prefix":"10.1007","volume":"9","author":[{"given":"Weijie","family":"Kang","sequence":"first","affiliation":[]},{"given":"Jiyang","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Junjie","family":"Xue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,20]]},"reference":[{"issue":"2","key":"787_CR1","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s40747-016-0019-3","volume":"2","author":"HM Elattar","year":"2016","unstructured":"Elattar HM, Elminir HK, Riad AM (2016) Prognostics: a literature review. 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