Based on the connected character of homologue

A pattern classification neural network model that can guarantee the correct connecting path of homologue is presented.This model includes the connecting and sequential technology of homologue samples and the sequential learning and incremental learing algorithms of topology sturcture of improved forward masking neural network model for establishment of connecting weight.The common potential problems of original sequential learning forward masking neural network model and many traditional pattern recognition methods that individual local connecting path of homologue are cut off can be overcome by this model which therefore enhance the categorizing ability.Moreover

this model can carry out rapid increamental learning for new added samples and hence is able to improve the categorizing and expanding ability of this model in a short time

which shows its advantage for large scale pattern recognition.Experiments also indicates that the pattern recognition model based on connectedness of homologue gives rise to a high correct recognition rate.This paper is of great significance to improving many traditional pattern recognition methods.