Data Pattern Recognition using Neural Network with Back-Propagation Training

In the last few years neural network is found as an effective tool for pattern recognition. The success rate for recognizing known and unknown pattern is relatively very high with compare to other techniques. This paper presents a comparative study of how neural network classifies the patterns from training data and recognizes if testing data holds that patterns. For learning from the training data lots of approaches are pres ent among which we have selected the back-propagation method. Back-propagation algorithm in a feed-forward network is used for the feature extraction. We have used two approaches and network was trained with specified data. We intended to find the match ratio of training Pattern to testing Pattern and the result data set found from the experiment also given in the paper.