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Neural Processing Letters

, Volume 44, Issue 3, pp 593–601 | Cite as

A New Geometry with Cross-Coupling of ART Networks

  • B. Lungsi Sharma
Article

Abstract

This paper demonstrates a new geometrical arrangement of adaptive resonance theory based network. Using method of minimal anatomies a neural network was constructed in an attempt to compare patterns. The anatomy incorporates two sub-networks coupled by feedback signals and an additional motor layer whose outputs reflect relationship or non-relationship among the compared patterns. Simulation results illustrates the network behaviors as emergent properties. The network with unsupervised learning is capable of generating self-defining features.

Keywords

Adaptive resonance theory Embedding field theory Minimal method anatomy Unsupervised learning Self-defining feature set Set-membership theory 

Notes

Acknowledgments

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The author is indebted to Dr. Richard B. Wells, emeritus professor at the University of Idaho, for valuable suggestions concerning the direction of this research. He would also like to thank Dr. Terence Soule, Dr. Eric Wolbrecht and Dr. Onesmo B. Balemba, all of University of Idaho, for helpful encouragement and criticisms.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Wells Laboratory of Computational Neuroscience (Asian Division)ImphalIndia

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