RECURSIVE DYNAMIC NODE CREATION IN MULTILAYER NEURAL NETWORKS

This paper presents the derivations of a novel approach for simultaneous recursive weight adaptation and node creation in multilayer back-propagation neural networks. The method uses time and order update formulations in the orthogonal projection method to derive a recursive weight updating procedure for the training process of the neural network and a recursive node creation algorithm for weight adjustment of a layer with added nodes during the training process. The proposed approach allows optimal dynamic node creation in the sense that the mean-squared error is minimized for each new topology. The effectiveness of the algorithm is demonstrated on several benchmark problems, namely, the multiplexer and the decoder problems as well as a real world application for detection and classification of buried dielectric anomalies using a microwave sensor.