A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
High capacity associative neural networks can be built from networks of perceptrons, trained using simple perceptron training. Such networks perform much better than those trained using the standard Hopfield one shot Hebbian learning. An experimental investigation into how such networks perform when the connection weights are not free to take any value is reported. The three restrictions investigated are: a symmetry constraint, a sign constraint and a dilution constraint. The selection of thesedoi:10.1080/09540090310001659981 fatcat:fdt2glxvn5bqfb7mbo6yjwcdbe