To simulate human cognitive process of repeated intercomparison and deliberate refinement

an image pattern cognition method and its calculative model with a simulated feedback mechanism are explored. The feature space is progressively optimized under the different cognitive demands via evaluative index system of cognitive result analysis. Layered feedback cognitive mode from global to local is realized. The model is successfully applied to burning state cognition of rotary kiln flame image. First

a simulated feedback mechanism-based image cognitive model with interactive running of training layer and cognitive layer is proposed;and the objection

structure and function of the model are elaborated. Second

analysis and evaluation of cognitive result-based simulated feedback mechanism is designed

and the evaluative index system of cognitive result analysis is constructed. Third

specific to the application of rotary kiln flame image-based burning state cognition

the original flame image feature space is constructed using KPCA to establish image pattern cognitive information system in the sense of maximum cognitive information content. Then

the compressed cognitive information system is established based on attribute core calculation and Mahalanobis metric function. Finally

under different cognitive demands

cognitive result is evaluated based on evaluative index system of cognitive result analysis to update the compressed cognitive information system

and simulated feedback intelligent cognition of burning state is realized. The simulation experimental study is carried out according to actual acquisition of collected rotary kiln flame image

and the average cognitive accuracy is 91.78%. Experiment results show that

relative to the existing open-loop method

our method cognates repeatedly similar samples which are hard to distinguish through the simulated feedback mechanism. Thus

cognitive accuracy is improved effectively.