Decorrelating parametrical neural network

We developed a new network architecture, allowing us to increase the recognizing characteristics of the Hopfield model substantively. In addition it is effective when recognizing correlated patterns. It is shown that the storage capacity of the network increases exponentially with increase of the free parameter of the problem. The boundaries restricting the increase of the storage capacity are defined.