Errors correction with optimised Hopfield neural networks

We present in this paper a method of increasing both the storage capacity of Hopfield neural networks and their capability of error correction. The presented method uses the general principles of generating error-correcting codes in information theory combined with a gradient - an heuristic algorithm. Using this method, there are registered improvements in the growth of network storage capacity (number of words memorized), and also in the increase of the correct answers´ probability. These types of networks can be used in several applications which use associativity, including the correction errors in communication, the image reconstruction and the object recognition.