Autoassociative-heteroassociative neural networks

The Autoassociative-Heteroassociative Neural Network (A-HNN) is a unique integration of autoassociative and heteroassociative neural network mappings to provide a functional approximation of two variables from one. This new architecture provides three features: the autoassociative mapping enables a stability metric for assessing the robustness or accuracy of the heteroassociative mapping; the A-HNN generates the inverse of the encoding portion of an associated autoassociative neural network (AANN); and, empirically, the use of input data as target vectors (the autoassociative mapping) improves training performance of the network. Published by Elsevier Science Ltd.