合成控制 meaning in Chinese
composite controls
Examples
- The synthetic control chart
合成控制图 - When the dosage was 8 % aa , the effect was best . ( 2 ) the performance of pc was decided with the proportion of pea , aa , mma . the fluidity of copolymer would augment if the dosage of aa increase , but when dosage of aa was 75 % , it was hard to control the compose reaction , and dispersion of pc would depress ; when pea degree of polymerization augment the air - entraining of mma would increase ; it would produce floe and hard to control the compose reaction when the dosage of pea4 more than 15 % , pea23 more than 25 %
( 2 ) pea 、 aa 、 mma之间的比例关系决定了共聚体的性能：随着丙烯酸用量的增加，共聚体的流动度增大，达到75时合成控制难度加大，共聚体分散性下降；而mma的引气增稳作用，其用量随着pea的聚合度的增加而增加， pea4体系的用量超过15 ， pea23体系超过25则会产生絮状物，反应控制难度加大，理想的用量分别为不超过10和15 。 - The important research is about the theory and methods of the cluster analysis in view of statistical theory , the theory and methods of fuzzy cluster analysis , the fkn " s structure and the fkn ' s study algorithm ( fkn , fuzzy kohonen network ) - the organic fusion of the fuzzy c - means algorithm and self - organized feature map neural network . the paper proposes the ifkn ( improved fkn ) on the basis of the hard classification idea and the soft classification idea , then carries on the cluster analysis of the artificial synthetic control chart time series through matlab program and tt ? cluster result matches the cluster result of the famous dataengine " s software of the intellectual data analysis and data mining from german mit company . finally , the paper discusses the applying of the cluster analysis to the control process , which can be widely applied to the pattern recognition of the parameter " s changing trend during the control process and the image partition processing , and utilizes the ifkn to recognize the thermotechnical parameter " s changing trend based on the engineering of clinker sintering rotary kiln automatic control system of guizhou " s aluminium factory , through which good effect is obtained
数据挖掘技术在商业领域中已广泛使用，然而在工业过程控制中的应用却极少，本文正是在这种背景下，对数据挖掘中的聚类分析方法及其在工业过程控制中的应用研究作了偿试，重点研究了基于统计理论的聚类分析理论和方法，模糊聚类分析理论和方法及模糊kohonen网络( fkn )的结构与学习算法，即模糊c ? ?均值算法与自组织特征映射神经网络( kohonen网络)的有机融合，并根据硬分类思想及软分类思想提出了改进的模糊kohonen网络( ifkn ) ，通过matlab编程对人工合成控制时序图数据集进行聚类分析，其聚类效果与当今广泛使用的数掘挖掘软件平台，德国mit公司著名的dataengine智能数据分析和数掘挖掘软件的聚类效果相当，最后，论述了聚类分析在控制中的应用，它可以用于过程控制中的参数变化趋势的模式识别及图象分割处理等具体应用中，并以贵州铝厂熟料烧结回转窑自动控制系统为工程背景，利用ifkn识别其热工参量变化趋势，取得了较理想的效果。