A NEURAL NETWORK ARCHITECTURE THAT COMPUTES ITS OWN RELIABILITY

Artificial neural networks (ANNs) have been used to construct empirical nonlinear models of process data. Because network models are not based on physical theory and contain nonlinearities, their predictions are suspect when extrapolating beyond the range of the original training data. With multiple correlated inputs, it is difficult to recognize when the network is extrapolating, Furthermore, due to non-uniform distribution of the training examples and noise over the domain, the network may have local areas of poor fit even when not extrapolating. Standard measures of network performance give no indication of regions of locally poor fit or possible errors due to extrapolation. This paper introduces the "validity index network" (VI-net), an extension of radial basis function networks (RBFN), that calculates the reliability and the confidence of its output and indicates local regions of poor fit and extrapolation. Because RBFNs use a composition of local fits to the data, they are readily adapted to predict local fitting accuracy. The VI-net can also detect novel input patterns in classification problems, provided that the inputs to the classifier are real values. The reliability measures of the VI-net are implemented as additional output nodes of the underlying RBFN. Weights associated with the reliability nodes are given analytically based on training statistics from the fitting of the target function, and thus the reliability measures can be added to a standard RBFN with no additional training effort.