Optimal Algorithmic Complexity of Fuzzy ART

Authors:
T. Burwick;F. Joublin
Affiliations:
Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany;Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany
Venue:
Neural Processing Letters
Year:
1998

Citing 2
Cited 3

Data Structures and Algorithms

Data Structures and Algorithms
Dynamic Model of Visual Memory Pedicts Neural Response Properties in the Visual Cortex

Dynamic Model of Visual Memory Pedicts Neural Response Properties in the Visual Cortex

Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks

EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Letters: Growing fuzzy topology adaptive resonance theory models with a push-pull learning algorithm

Neurocomputing
A supervised fuzzy adaptive resonance theory with distributed weight update

ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We discuss implementations of the Adaptive Resonance Theory (ART) on aserial machine. The standard formulation of ART, which was inspired byrecurrent brain structures, corresponds to a recursive algorithm. Thisinduces an algorithmic complexity of order O(N^2)+O(MN) inworst and average case, N being the number of categories, and M the inputdimension. It is possible, however, to formulate ART in a non-recursivealgorithm such that the complexity is of order O(MN) only.