A Robust-ELM Approach Based on Parzen Windiow's Estimation for Kiln Sintering Temperature Detection
一种基于 Parzen 窗估计的鲁棒 ELM 烧结温度检测方法

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.