Weight error sensitivity of fixed point attractors in associative memory networks
The sensitivity of Hopfield neural networks, with two-state neurons is investigated. Simple expressions are derived which give the probability that an equilibrium point of the nominal connection matrix remains a fixed point and an attractor, as a function of the relative error in the weights. Such probability decreases as the number of neurons in the network and the number of stored patterns increase.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.