Hill-Climbing, Density-Based Clustering and Equiprobabilistic Topographic Maps
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A Pattern Reordering Approach Based on Ambiguity Detection for Online Category Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A hybrid intelligent system for multiobjective decision making problems
Computers and Industrial Engineering - Special issue: Computational intelligence and information technology applications to industrial engineering selected papers from the 33 rd ICC&IE
Review: Meta knowledge of intelligent manufacturing: An overview of state-of-the-art
Applied Soft Computing
A hybrid system for multiobjective problems - A case study in NP-hard problems
Knowledge-Based Systems
Modified ART2A-DWNN for Automatic Digital Modulation Recognition
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A hybrid intelligent system for multiobjective decision making problems
Computers and Industrial Engineering
Fuzzy Neural Network with a Fuzzy Learning Rule Emphasizing Data Near Decision Boundary
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A hybrid systematic design for multiobjective market problems: a case study in crude oil markets
Engineering Applications of Artificial Intelligence
Colour image segmentation using the self-organizing map and adaptive resonance theory
Image and Vision Computing
Automatic digital modulation recognition based on ART2A-DWNN
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Reconfigurable networked fuzzy takagi sugeno control for magnetic levitation case study
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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Adaptive resonance theory (ART) describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. Many different types of ART networks have been developed to improve clustering capabilities. We compare clustering performance of different types of ART networks: fuzzy ART, ART 2A with and without complement encoded input patterns, and a Euclidean ART 2A-variation. All types are tested with two- and high-dimensional input patterns in order to illustrate general capabilities and characteristics in different system environments. Based on our simulation results, fuzzy ART seems to be less appropriate whenever input signals are corrupted by addititional noise, while ART 2A-type networks keep stable in all inspected environments. Together with other examined features, ART architectures suited for particular applications can be selected