文章基本信息

标题：A Sliding-Window T-S Fuzzy Neural Network Model for Prediction of Silicon Content in Hot Metal 1 1 This work is supported by the National Science Foundation of China(61290321).
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作者：Heng Zhou ; Chunjie Yang ; Wenhui Liu 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：14988-14991
DOI：10.1016/j.ifacol.2017.08.2564
语种：English
出版社：Elsevier
摘要：AbstractIron making in blast furnace is one of the most complicated industrial processes, especially in its dynamics, inertial properties and multi-scale availabilities. Over the years, researchers have been using silicon content to judge the temperature and the conditions within the blast furnace due to the complexity in measuring the actual status that results from extreme temperatures and intricate environment. Addressing these limitations, a sliding-window Takagi-Sugeno fuzzy neural network(SW-TS FNN) model is proposed to predict the silicon content in hot metal. Through the sliding of a proper width of the sliding-window, the train data for T-S fuzzy neural network(FNN) model can be updated at desired time increments, giving the latest prediction of silicon content. Compared to a simple T-S FNN model on the prediction of silicon content, this SW-TS FNN model shows great improvement at hit rate and mean-square error.
关键词：KeywordsFuzzy Neural NetworkSliding-WindowPredictionSilicon ContentBlast Furnace