title: Sensitivity analysis of single hidden-layer neural networks with threshold functions.
abstract: An important consideration when applying neural networks to pattern recognition is the sensitivity to weight perturbation or to input errors. In this paper, we analyze the sensitivity of single hidden-layer networks with threshold functions. In a case of weight perturbation or input errors, the probability of inversion error for an output neuron is derived as a function of the trained weights, the input pattern, and the variance of weight perturbation or the bit error probability of the input pattern. The derived results are verified with a simulation of the Madaline recognizing handwritten digits. The result shows that the sensitivity of trained networks is far different from that of networks with random weights.
authors.affiliation: Res. Dept., Electron. and Telecommun. Res. Inst., Daejeon.
authors.forename: S H
authors.initials: SH
authors.lastname: Oh
authors.forename: Y
authors.initials: Y
authors.lastname: Lee