粗集理论在薄膜蒸发器工况预警中的应用  

Application of Rough set theory on operating early warming of Thin-film Evaporator

作　　者：马洪明[1] 李庆生[1] 李永生[1] 

机构地区：[1]南京工业大学机械与动力工程学院,南京210009

出　　处：《微计算机信息》2009年第32期33-35,共3页Control & Automation

摘　　要：本文以粗糙集理论为基础,运用可辨识矩阵对薄膜蒸发器参数的特征参数进行约简,过滤冗余参数,结合神经网络学习方法,用ActiveXDLL和COM组件生成技术实现了VisualBasic与Matlab的链接,建立一种薄膜蒸发器工况预警系统,并对工况预测研究。结果表明,该系统提高了对薄膜蒸发器工况的预警的效率和正确性,为实际应用以及开发智能薄膜蒸发器工况预警系统创造条件。In order to establish a thin-film evaporator operating early warming system based on the rough set theory and predicts the working conditions of the thin-film evaporator, it is appropriate the to simplify the characteristic parameters using the method of discernibility matrix, integrate the algorithm of neural network and adopt the generation technique of ActiveX DLL and COM components to connect Visual Basic and Matlab. The result shows that the proposed system can improve efficiency and the accuracy of thin-film evaporator operating early warming for the practical application and the development of the intellective operating early warming systems of the thin-film evaporator.

关 键 词：粗糙集理论 神经网络 薄膜蒸发器 工况预警 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O144[自动化与计算机技术—控制科学与工程]