Name
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. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | Adaptive Resonance Theory, Particle Swarm Optimization, Validation Indexes |
Field | DocType | ISSN |
Particle swarm optimization,Adaptive resonance theory,Cluster (physics),Computer science,Fuzzy logic,Vigilance (psychology),Multi-swarm optimization,Artificial intelligence,Resonance,Machine learning,Centroid | Conference | 2161-4393 |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clayton Smith | 1 | 1 | 0.70 |
| Wunsch II Donald C. | 2 | 1354 | 91.73 |