Efficient Numeral VG-RAM Pattern Recognition Using Manhattan Distance Calculation and Minimization Algorithm

Ivan A. Hashim , Prof. Dr. Jafar W. Abdul Sadah and Assist. Prof. Dr. Thamir R. Saeed

Pattern recognition is one of the important tools in the automation industry. Many techniques have been used to achieve this task. One of these techniques is the Virtual Generalizing Random Access Memory (VG-RAM). The weakness of this technique appears when the input is not binary. Therefore, to overcome the VG-RAM weakness, the Manhattan distance has been used instead of Hamming distance in this paper. Also, a reduction in the classification time was achieved using a minimization algorithm. The combination of these two methods takes 0.03 sec. to classify 283 input sets compared to 5.913 and 0.551 sec. using MLP and SVM methods respectively. the number of training sets has been reduced from 300 to 32 with a similarity measure reduction from 1 to 0.3. in addition, the number of occupied slices in FPGA implementation was reduced from 1557 to 976 with a probability of correct classification from 98.6% to 96.4%.