Recursive dynamic node creation in multilayer neural networks Azimi-Sadjadi, Mahmood R. ; Sheedvash, Sassan ; Trujillo, Frank O. 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. Colorado State University. Libraries 1993 text ; image application/pdf ECEmra00047.pdf FACFECEN100527ARTI eng c1993 IEEE
Recursive dynamic node creation in multilayer neural networks
Azimi-Sadjadi, Mahmood R. ; Sheedvash, Sassan ; Trujillo, Frank O.
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.
Colorado State University. Libraries
1993
text ; image
application/pdf
ECEmra00047.pdf
FACFECEN100527ARTI
eng
c1993 IEEE