Self-Organization of Architecture by Simulated Hierarchical Adaptive Random Partitioning

Proceedings of the International Joint Conference on Neural Networks

Volumn 3, Issue , 1992, Pages 823-828

  Banan, M R ^a   Hjelmstad, K D ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MULTILAYER NEURAL NETWORKS;

CONFIDENCE INDICES; CONNECTION WEIGHTS; GENERALISATION; HIDDEN LAYERS; MODULARS; NEURAL-NETWORKS; SELF ORGANIZATIONS; SELF-ORGANISING; SIMULATION ENVIRONMENT; SMALL DATA SET;

NETWORK ARCHITECTURE;

EID: 46449092340     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.1992.227050     Document Type: Conference Paper

References (5)

1. Ash, T. (1989). Dynamic node creation in backpropagation networks. ICS Report 8901. Institute of Cognitive Science. University of California in San Diego. La Jolla, CA.
2. Bañan, M. R. and Hjelmstad, K. D. (1991). A hierarchical adaptive random partitioning method for bunding trained feedforward neural networks. Proceeding of the Erst Artificial Neural Networks in Engineering Conference. St. Louis, MO. November. 217-222.
3. Baum, E. B. (1989). A proposal for more powerful learning algorithms. Neural Computation. 1. 201-207
4. Kamin, E . D. (1990). A simple procedure for pruning backpropagation trained neural networks. IEEE Transactions on Neural Networks. 1. 239-242.
5. Lee, S., and Kil, M. R. (1991). A Gaussian potential function network with hierarchically self-organizing learning. Neural Networks. 4. 207-224.