Abstract 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.
2015, Vol. 28 
