Analysis and synthesis of discrete-time neural networks with multilevel threshold functions

In contrast to the usual types of neural networks which utilize two states for each neuron, a class of synchronous discrete-time multilevel threshold neural networks is developed. A qualitative analysis and a synthesis procedure of the class of neural networks constitute the principal contributions of this work. The applicability of the class of neural networks is demonstrated by means of a gray-level image processing example in which each neutron of the present model assumes one of 16 values. In doing so, the number of neurons and the number of interconnections are reduced, when compared to the usual binary state networks