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 problem of decision function in classification phase.The fuzzy lattice inclusion measure function is introduced to calculate the membership of testing sample belong to the hyper-boxes trained by the CMNN.Three standard datasets are employed to evaluate and compare the presented FL-CMNN with the CMNN

artificial neural network(ANN)

support vector machine(SVM)and K nearest neighbor(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.