Improving ARTMAP learning through variable vigilance.
This paper presents a mechanism to vary the vigilance parameter in the RePART fuzzy neural network. This mechanism helps to smooth out the problem of category proliferation which affects ARTMAP-based networks. Empirical experiments show that the use of variable vigilance improves the performance of the RePART model while, at the same time, requiring a less complex structure.
Canuto
A
A
Electronic Engineering Laboratory, University of Kent, Canterbury, Kent CT27NT, UK. amdc1@ukc.ac.uk
Fairhurst
M
M
Howells
G
G
D000465:Algorithms; D001185:Artificial Intelligence; D001288:Attention; D019992:Databases as Topic; D017143:Fuzzy Logic; D007858:Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D011678:Punishment; D012201:Reward; D012718:Set, Psychology