Title

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.

Meeting Name

International Joint Conference on Neural Networks, IJCNN 2015 (2015: Jul. 12-17, Killarney, Ireland)

Department(s)

Electrical and Computer Engineering

International Standard Book Number (ISBN)

9781479919604

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.


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