基于变粒度仿反馈机制的回转窑烧成状态智能认知方法  

Variable Granularity and Simulated Feedback Mechanism Based Burning State Intelligent Cognitive Method of Rotary Kiln Sintering Process

作　　者：陈克琼[1] 王建平[1] 李帷韬[1] 赵丽欣[1] 

机构地区：[1]合肥工业大学电气与自动化工程学院,合肥230009

出　　处：《模式识别与人工智能》2015年第11期1013-1022,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家青年自然科学基金项目(No.61305029);中国博士后科学基金面上项目(No.2013M541820;2013M532118);中央高校基本科研业务费专项资金项目(No.2013HGBH0010;2013HGQC0012);安徽省青年自然科学基金项目(No.1408085QF133)资助

摘　　要：采用改进的压缩Gabor滤波器组对火焰图像进行预处理,由比例不变特征变换描述符、视觉单词本和潜在语义分析结合的方法提取火焰图像感兴趣区域局部形态特征.在给定的认知信息粒度层次中基于特征分辨度、认知粒度熵和特征权值的定义,构建相应的简约特征空间.生成多类训练样本的多维逆向正态粒子云模型,并基于云隶属度构造模式分类器获取回转窑烧成状态分类规则.基于认知误差的定义,给出基于变粒度仿反馈机制的回转窑烧成状态智能认知方法.实验表明,文中方法对回转窑烧成状态认知效果较优.An improved compressed Gabor filter bank is used for flame image pre-processing, and the scale-invariant feature transform descriptor is combined with bag of visual words and latent semantic analysis to extract the local configuration features of the flame image region of interests. A simple feature space is constructed based on the definition of feature resolution, cognitive granular entropy, and feature weight in the given level of cognitive information granularity. The multi-dimensional reverse normal particle cloud model of training samples is generated and the pattern classifier is constructed based on cloud-membership to obtain the burning state classification rules of rotary kiln sintering process. Variable granularity and simulated feedback mechanism based burning state intelligent cognitive method of rotary kiln sintering process is presented based on the definition of cognitive error. Experiments show that the proposed method is superior in cognizing the burning state to other methods.

关 键 词：回转窑烧成状态认知 变粒度 仿反馈 云模型 认知误差 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]