  Neural cryptography is the application of artificial neural networks in the
subject of cryptography. The functionality of this solution is based on a tree
parity machine. It uses artificial neural networks to perform secure key
exchange between network entities. This article proposes improvements to the
synchronization of two tree parity machines. The improvement is based on
learning artificial neural network using input vectors which have a wider range
of values than binary ones. As a result, the duration of the synchronization
process is reduced. Therefore, tree parity machines achieve common weights in a
shorter time due to the reduction of necessary bit exchanges. This approach
improves the security of neural cryptography
