Abstract
A digital methodology called single error correction-single error detection (SECSED) is used to improve the learning and recall rules of the conventional Hopfield neural network. The XOR operator is used to implement the learning phase and the recall phase of the modified network. The bulk data processing of the modified Hopfield network is much faster than that of the conventional Hopfield network, because the improved method uses only XOR operations to avoid the carry-propagation of the multiplication and addition which is necessary in the learning and recall phases in the conventional Hopfield network. Because only the XOR operation is involved, it is very suitable for VLSI implementation of the neural network. Simulated results indicate that the modified Hopfield neural network can recognize each 24% noise-interfered pattern within 1 to 6 iterations with a recognition rate above 90%, while the conventional Hopfield neural network recognizes each small noise-interfered pattern with 6 to 12 iterations and a recognition rate below 5%. >
Published Version
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