A new approach for dynamic node creation in multilayer neural networks

An approach to simultaneous recursive weight adaptation and node creation in multilayer perceptron neural networks is presented. The method uses time and order update formulations in the orthogonal projection method to arrive at 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 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 was demonstrated on a real world application for detecting and classifying underground dielectric anomalies