Particle Swarm Optimization in an Adaptive Resonance Framework
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.
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
International Joint Conference on Neural Networks, IJCNN 2015 (2015: Jul. 12-17, Killarney, Ireland)
Electrical and Computer Engineering
Center for High Performance Computing Research
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2015