J. Si et An. Michel, ANALYSIS AND SYNTHESIS OF A CLASS OF DISCRETE-TIME NEURAL NETWORKS WITH MULTILEVEL THRESHOLD NEURONS, IEEE transactions on neural networks, 6(1), 1995, pp. 105-116
In contrast to the usual types of neural networks which utilize two st
ates for each neuron, a class of synchronous discrete-time neural netw
orks with multilevel threshold neurons is developed. A qualitative ana
lysis and a synthesis procedure for the class of neural networks consi
dered herein constitute the principal contributions of this paper. The
applicability of the present class of neural networks is demonstrated
by means of a gray level image processing example, where each neuron
can assume one of sixteen values. When compared to the usual neural ne
tworks with two state neurons, networks which are endowed with multile
vel neurons will in general, for a given application, require fewer ne
urons and thus fewer interconnections. This is an important considerat
ion in VLSI implementation.