一种基于 Parzen 窗估计的鲁棒 ELM 烧结温度检测方法

在回转窑燃煤火焰视频模糊且干扰较大的情况下, 基于火焰辐射能量和燃烧稳定程度提取多帧煤粉燃烧图像的统计特征进行烧结温度判断. 为克服工业现场特征数据中的粗差干扰,将极限学习机(Extreme learning machine, ELM)与稳健估计理论相结合, 用训练误差分布的Parzen窗非参数估计构造ELM权矩阵,对其输出层权值进行稳健最小二乘估计. 基于上述火焰视频的统计特征,用该改进的鲁棒极限学习机(Robust-ELM)检测烧结带温度.实验结果表明, 在视频图像模糊、不能用常规静态图像处理方法软测量烧结带温度时,本文方法可快速有效地检测窑内烧结温度, 且检测系统不易受现场干扰,稳定性强.

A Robust-ELM Approach Based on Parzen Windiow's Estimation for Kiln Sintering Temperature Detection

To eliminate the interference in the blurring pulverized coal flame image sequences of rotary kiln, a new kiln sintering temperature measurement method based on statistical features of pulverized coal flames and robust extreme learning machine (robust-ELM) is proposed in this paper. The degree of stability and quantity of radiant energy are computed from a blurry flames image sequences as statistical features, robust-ELM is presented to estimate the sintering temperature based on the above features of flames image. The distribution of training error of extreme learning machine (ELM) is estimated by Parzen windows to make up the weighted matrix to reduce the disturbance of gross errors in industrial field. Finally, a series of tests were undertaken on an industrial-scale flame videos, which showed the methods could measure sintering temperature more accurately, quickly, and robustly.