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In this paper, an efficient second order algorithm for long short-term memory (LSTM) network training is proposed for chemical process intelligent identification. A novel Hessian inverse recursion method is adopted to achieve fast convergence and avoid the high-cost operation of the classic second order optimization method. Besides, more information is back propagated since the proposed method retains the real curvature information of the neural network. Considering the large amount of chemical process data, a sub-sampled recursive second order-stochastic gradient descent (SRSO-SGD) algorithm which uses sub-sampling method and hybrid strategy is proposed. The identification experiment on a delayed coker fractionator shows that the proposed sub-sampled neural network second order training algorithm has better performance than other learning algorithms in terms of model identification accuracy and convergence speed. By adopting a hybrid strategy that performing Hessian inverse estimation every 3 training epochs, the expensive Hessian inverse calculation cost in the identification process is further reduced while low training and testing errors are maintained.<\/jats:p>","DOI":"10.1007\/s44196-023-00296-5","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T17:02:10Z","timestamp":1689872530000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Hybrid Strategy Enhanced Sub-Sampled Recursive Second Order Algorithm for Chemical Process Intelligent Identification"],"prefix":"10.1007","volume":"16","author":[{"given":"Yaxin","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baochang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"296_CR1","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.jtice.2016.09.007","volume":"73","author":"JH Chen","year":"2017","unstructured":"Chen, J.H., Gu, S.W.: Development of LTV subspace system identification using basis functions approach to assessing the performance of control loops for nonlinear processes. 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