Error and variance bounds on sigmoidal neurons with weight and input errors
暂无分享,去创建一个
Bounds on the expectation and variance of errors at the output of a multilayer feedforward neural network with perturbed weights and inputs are derived. It is assumed that errors in weights and inputs to the network are statistically independent and small. The bounds obtained are applicable to both digital and analogue network implementations and are shown to be of practical value.
[1] Athanasios Papoulis,et al. Probability, Random Variables and Stochastic Processes , 1965 .