A new learning algorithm is proposed for space-varying cellular neural netw
orks, used to implement associative memories. The algorithm exhibits some p
eculiar features which make it very attractive: the finite precision of con
nection weights is automatically taken into account as a design constraint;
no multiplication is needed for weight computation; learning can be implem
ented in fixed point digital hardware or simulated on a digital computer wi
thout numerical errors.