Optimal dimensioning of counter propagation neural networks

Abstract:

The absence of automated tools in the area of automated neural network design can be explained by the corresponding paucity of rigorous neural network composition techniques. The author suggests a hybrid architecture as the basis for a computer-aided neural network engineering tool. Such a tool is expected to complete automatically the minute yet important neural network architecture details. The author demonstrates the approach by developing an automatic counterpropagation neural network design module. It includes a mechanized Kohonen layer configurator, which combines A* and simulated annealing search techniques to achieve both automated dimensioning of the layer and simultaneous selection of its weights.

Published in: IJCNN-91-Seattle International Joint Conference on Neural Networks
Date of Conference: 08-12 July 1991
Date Added to IEEE Xplore06 August 2002
Print ISBN:0-7803-0164-1
DOI: 10.1109/IJCNN.1991.155376
Publisher: IEEE
Conference Location: Seattle, WA, USA