目的 模仿人类认知事物反复推敲比对的过程,探索了一种具有仿反馈机制的图像模式分类认知方法和计算模型,通过认知结果分析评价指标体系在不同认知需求下逐级优化特征空间,实现从全局到局部的仿反馈认知模式,并成功应用于回转窑火焰图像的烧成状态认知。方法 首先,提出了训练层和认知层耦合运行的具有仿反馈机制的图像模式分类认知模型,阐述了模型的目标、结构和功能;其次,设计了基于认知结果分析评价的仿反馈运行机制,建立了认知结果分析评价指标体系;然后,针对火焰图像烧成状态认知的应用,采用核主成分分析技术(KPCA)构建初始火焰图像特征空间,以建立最大认知信息量意义下的图像模式分类认知信息系统,继而基于属性核计算和马氏可分性评价指标建立压缩的认知信息系统;最后,在不同认知需求下,基于分析评价指标体系评估认知结果,更新压缩认知信息系统,实现烧成状态仿反馈智能认知。结果 对采集的火焰图像进行了仿真实验研究,平均认知精度达到91.78%。结论 实验结果表明,相对于已有的开环认知方法,本文方法可以通过仿反馈机制对难以区分相似样本进行反复认知,有效改进了认知精度。

Objective 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. Method 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. Result 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%. Conclusion 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.