电子学报Issue(2)：319-327,9.DOI:10.3969/j.issn.0372-2112.2014.02.017
模糊格构造型形态神经网络
Fuzzy Lattice Constructive Morphological Neural Network
摘要
Abstract
A novel neural network model named fuzzy lattice constructive morphological neural network (FL-CMNN ) is presented to overcome the deficiency of the original constructive morphological neural network (CMNN ) ,which suffers for the prob-lem of decision function in classification phase .The fuzzy lattice inclusion measure function is introduced to calculate the member-ship of testing sample belong to the hyper-boxes trained by the CMNN .Three standard datasets are employed to evaluate and com-pare the presented FL-CMNN with the CMNN ,artificial neural network (ANN ) ,support vector machine (SVM )and K nearest neigh-bor(KNN)classifiers .Experimental results have revealed that the presented FL-CMNN yields better performance than the original CMNN model .It also achieved comparative classification accuracies with much lower computational cost than traditional ANN and SVM model .关键词
数学形态学/形态神经网络/模糊格/模式识别Key words
mathematical morphology/morphological neural network (MNN)/fuzzy lattice/pattern recognition分类
信息技术与安全科学引用本文复制引用
李兵,董俊,刘鹏远,米双山..模糊格构造型形态神经网络[J].电子学报,2014,(2):319-327,9.基金项目
国家自然科学基金 （）
