Abstract

One of the key factors which limits the use of neural networks in many industrial applica­tions has been the difficulty of demonstrating that a trained network will continue to generate reli­able outputs once it is in routine use. An import­ant potential source of errors is novel input data; that is, input data which differ significantly from the data used to train the network. The author investigates the relationship between the degree of novelty of input data and the corresponding reliability of the outputs from the network. He describes a quantitative procedure for assessing novelty, and demonstrates its performance by using an application which involves monitoring oil flow in multiphase pipelines.