一种新的灰关联分析算法在软测量中的应用

Application of a Novel Grey Relation Analysis Algorithm to Soft Sensor

软测量是一种有效的在线测量技术,它在工业生产中受到越来越广泛的重视．作为实施软测量的前提,辅助变量的选择至关重要,它将直接影响软测量的效率．灰关联分析是一种研究变量间相关性的新方法,它适于求解不确定性系统中小样本数据之间的关联程度．深入研究了软测量辅助变量选择过程中存在的问题,基于灰关联分析,提出了一致关联度算法,对辅助变量进行选择．并给出了一种实用的冗余变量剔除方法．同时,结合仿真实例对其有效性进行验证．仿真结果表明该方法是有效可行的,有助于提高软测量的精度．

As an effective technique of on-line measurement, soft sensor has been studied by many efforts. Being the premise of soft sensor, it is important to select secondary variables, which can directly influence the efficiency of soft sensor. Grey relation analysis gives a novel method to dig correlation between variables and is suitable to confirm the correlation degree between little-sample data in uncertain systems. Through a detailed analysis of secondary variables＇ selection for soft sensor, a uniform incidence degree algorithm is presented based on grey relation theory to efficiently select secondary variables. And a practical method to eliminate redundant variables is also proposed. Meanwhile, a practical example is given to verify the feasibility and effectiveness of the method. Simulation results show that the method is efficient and helpful to enhance the accuracy of soft sensor.