Feature Variables Based Predicting Modelfor Complex Production Process
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摘要: 研究了一種基于特征變量的復雜生產過程預測模型.與傳統的建模方法相比,該方法不需要經過機理分析,而從信息科學的角度出發,在對反映生產過程工況原始動態數據進行特征選擇的基礎上,運用時間序列分析法建立其預測模型.同時討論了它的神經網絡實現方法.仿真結果表明了該方法的可行性.Abstract: A feature variables based predicting model for complex production process was presented. Comparing with traditional methods,it bypasses the mechanism analysis,using time series analysis technology and feature variables,selected from original dynamicaldatad which can reflect the operating mode of complex production process build a new kind of predicting model.The neural network realization of this model is discussed.The results obtained by simulation show the feasible of this method.
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Key words:
- feature selection /
- predicting model /
- neural network
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