Application of fuzzy algorithm based on distance in hand-shape dentification

The problem of fuzzy pattern recognition based on eigenvector is often met with in computer measurement and control system. Both the object to be identified and the standard pattern stored in the database have a certain degree of uncertainty because errors are inevitable in the process of eigenvector extraction. The problem of fuzzy pattern recognition can be turned into the problem of calculating the hamming distance and Euclidean distance of two fuzzy sets according to fuzzy theory. This thesis aims to describe the fuzzy pattern recognition algorithm based on distance of two fuzzy sets, which is successfully used in the hand-shape identity recognition system by using the normal distribution function as the membership function of the eigenvector. Experiment shows that this algorithm can be promoted and applied to any other pattern recognition system in which random errors are inevitable.