Particle Swarm Optimization in an Adaptive Resonance Framework
Abstract
A Particle Swarm Optimization (PSO) technique, in conjunction with Fuzzy Adaptive Resonance Theory (ART), was implemented to adapt vigilance values to appropriately compensate for a disparity in data sparsity. Gaining the ability to optimize a vigilance threshold over each cluster as it is created is useful because not all conceivable clusters have the same sparsity from the cluster centroid. Instead of selecting a single vigilance threshold, a metric must be selected for the PSO to optimize on. This trades one design decision for another. The performance gain, however, motivates the tradeoff in certain applications.
Recommended Citation
C. Smith and D. C. Wunsch, "Particle Swarm Optimization in an Adaptive Resonance Framework," Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2015.
The definitive version is available at https://doi.org/10.1109/IJCNN.2015.7280585
Meeting Name
International Joint Conference on Neural Networks, IJCNN 2015 (2015: Jul. 12-17, Killarney, Ireland)
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
International Standard Book Number (ISBN)
978-1479919604
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2015
