During the implementation process of artificial neural networks, devia
tions from the desired ideal neural network are frequently introduced.
These include parameter perturbations, transmission delays, and inter
connection constraints. In the present article, we study the effects o
f these realities of imperfection on the qualitative behavior of artif
icial feedback neural networks. To accomplish this, we utilize a speci
fic class of neural networks (Hopfield-like neural networks) with a sp
ecific application (the realization of associative memories) as a vehi
cle for our study. The principal issues which we address concern the e
ffects of parameter perturbations, transmission delays, and interconne
ction constraints on the accuracy and on the qualitative properties of
the network memories.