Self-Organization of Architecture by Simulated Hierarchical Adaptive Random Partitioning

M. R. Banan, K. D. Hjelmstad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

In this article we present a simulation environment, based on the concept of hierarchical adaptive random partitioning (HARP), for simultaneously self-organizing the architecture and connection weights of neural networks to approximate multivariate mappings. The constructed approximation can be modeled as a modular, feedforward neural network with two hidden layers. The proposed environment shows good generalization even for small data sets and computes a confidence index for its predicted output. The simulation environment has a fast, automatic learning process and is based on a sound mathematical foundation.

Original languageEnglish (US)
Title of host publicationProceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages823-828
Number of pages6
ISBN (Electronic)0780305590
DOIs
StatePublished - 1992
Externally publishedYes
Event1992 International Joint Conference on Neural Networks, IJCNN 1992 - Baltimore, United States
Duration: Jun 7 1992Jun 11 1992

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume3

Conference

Conference1992 International Joint Conference on Neural Networks, IJCNN 1992
Country/TerritoryUnited States
CityBaltimore
Period6/7/926/11/92

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Self-Organization of Architecture by Simulated Hierarchical Adaptive Random Partitioning'. Together they form a unique fingerprint.

Cite this