Dignet: an unsupervised-learning clustering algorithm for clustering and data fusion

Dignet is a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior-to-noise interference, when the noise does not exceed a prespecified level of tolerance. The complexity of the proposed ANN, in terms of neuron requirements versus stored patterns, increases...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 1995-01, Vol.31 (1), p.21-38
Main Authors: Thomopoulos, S.C.A., Bougoulias, D.K., Chin-Der Wann
Format: Article
Language:English
Subjects:
Applied sciences
Artificial neural networks
Clustering algorithms
Control systems
Exact sciences and technology
Fusion power generation
Information, signal and communications theory
Interference
Maximum likelihood detection
Miscellaneous
Neurons
Noise level
Research and development
Signal and communications theory
Telecommunications and information theory
Unsupervised learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Dignet is a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior-to-noise interference, when the noise does not exceed a prespecified level of tolerance. The complexity of the proposed ANN, in terms of neuron requirements versus stored patterns, increases linearly with the number of stored patterns and their dimensionality. The self-organization of Dignet is based on the idea of competitive generation and elimination of attraction well in the pattern space. Dignet is used for detection and distributed decision fusion. Analytical and numerical results are included.< >
ISSN:0018-9251
1557-9603
DOI:10.1109/7.366289