title: Sensitivity of feedforward neural networks to weight errors.
abstract: An analysis is made of the sensitivity of feedforward layered networks of Adaline elements (threshold logic units) to weight errors. An approximation is derived which expresses the probability of error for an output neuron of a large network (a network with many neurons per layer) as a function of the percentage change in the weights. As would be expected, the probability of error increases with the number of layers in the network and with the percentage change in the weights. The probability of error is essentially independent of the number of weights per neuron and of the number of neurons per layer, as long as these numbers are large (on the order of 100 or more).
authors.affiliation: Dept. of Electr. Eng., Stanford Univ., CA.
authors.forename: M
authors.initials: M
authors.lastname: Stevenson
authors.forename: R
authors.initials: R
authors.lastname: Winter
authors.forename: B
authors.initials: B
authors.lastname: Widrow