Recursive node creation in back-propagation neural networks using orthogonal projection method

The authors present the derivations of a novel approach for recursive weight adaptation and node creation in multilayer perceptron 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 after the training process. The proposed approach allows optimal dynamic node creation in the sense that the mean-squared error is minimized for each new architecture