Identifying Datasets by Pattern Recognition Techniques using Neural Networks

Abstract

The process of Pattern Recognition neural network is found to be an effective tool. Different combinations of neural networks are used for this technique. Earlier works are based on only back propagation network but the proposed work is based on the combination of Hopfield network and Back propagation network. The success rate for recognizing known and unknown pattern is relatively very high with this combination of networks when compare to other techniques. As the fault tolerance of the Hopfield network is more than back propagation so required percentage accuracy of output of the new combined network should be more as compared to only back propagation network. The objective of the neural network is to transform the inputs into meaningful outputs. The signals are transmitted by the means of connections links. It is inspired by the character set nervous connected system. We proposed new techniques for better character set reorganization using various implementation methods evolution analysis and identification needs. It is very helpful and vast scope for future research.

Keywords

Pattern Recognition, Hopfield network, Back Propagation Network, Training Set, Fault Tolerance.

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