A Pattern Recognition Method based on Statistical Mapping Space
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摘要: 采用回歸分析的方法,建立特征變量與產品質量之間的統計對應關系,把產品質量表達成特征變量的回歸函數,進而得到特征空間與產品質量空間在統計意義上的映射關系.在產品質量空間進行聚類,在特征空間進行分類,而后提出了一種基于統計空間映射的在線模式識別方法.利用唐鋼燒結廠的實測數據進行了仿真,驗證了本方法的正確性.從算法分析和仿真結果看,這一算法可以有效地克服模式交叉現象的影響,并可對復雜生產過程進行在線質量推斷.Abstract: The statistical relationship between feature vector and quality index is built by regress analysis and then the quality index is expressed to the regress function of feature vector. By this means, a statistical mapping relation between in.feature space and quality index space is built and a online pattern recognition method based on the statistical mapping space is provided through the clustering in quality space and classification in feature space. The validity of the method is verified by the simulation results of the data from Sinter factory of Tangshan Steel Corporation. From the algorithm analysis and simulation results, this method can effectively overcome the pattern intercross and can be used for complex production process quality online prediction.
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