关键词: 构造性形态学神经网络, 提升算法, 分类

Abstract: Constructive Morphology Neural Network（CMNN） is a kind of new nonlinear neural network which is the combination of mathematical morphology and the traditional neural network model and has a strong practicability. Its training algorithm is derived according to the morphological associative memory. Morphological operator is used to rank the test sample to the box in the process of testing. But it is unable to classify this sample which is out of the box into correct group. Someone presents a morphology neural network based on fuzzy lattice（FL-CMNN）. This algorithm can improve effect of classification by calculating the membership grade sample of sample and box. But the complexity of the algorithm is increased and the result is unstable. A kind of lifting algorithm based on morphological neural network（LCMNN） has been presented. This algorithm inherits the quick operation speed of original morphological operator and is able to classify this sample which is out of the box into correct group. Numerical experiments show that LCMNN has the best effect of classification and is simple, with less computation time compared with several other algorithms.

Key words: constructive morphology neural network, ascension algorithms, classification