Noise Removal Using Hopfield Neural Network in Message Transmission Systems
D. Gladis, P. Thangavel · 2008
In this paper, a novel approach of two-tier encryption is proposed with the removal of noise generated during transmission based on Hopfield neural networks (HNN). The proposed system reduces the complexity of recognition of characters due to external distortion or diffusion. Though there are many error correction and detection codes, these codes request retransmission when there is an error. If the error rate is high the number of retransmissions are high which causes a delay in the process of communicating the information. Moreover the error correction systems can prevent the systems from data loss but will not help in recognition of letters if diffused. When HNN is added to the existing system, the learning ability enables the network to understand or even remember the pattern. But when images of larger size are stored, the network fails to recognize, which leads to further research in this area.