Optimal algorithmic complexity of fuzzy ART

被引：15
|
作者：
Burwick, T [1 ]
Joublin, F [1 ]
机构：
[1] Ruhr Univ Bochum, Inst Neuroinformat, D-44780 Bochum, Germany
来源：
NEURAL PROCESSING LETTERS | 1998年 / 7卷 / 01期
关键词：
adaptive resonance theory; algorithmic complexity; fuzzy systems; neural networks; unsupervised learning;
D O I：
10.1023/A:1009632604848
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We discuss implementations of the Adaptive Resonance Theory (ART) on a serial machine. The standard formulation of ART, which was inspired by recurrent brain structures, corresponds to a recursive algorithm. This induces an algorithmic complexity of order O(N-2)+O(MN) in worst and average case, N being the number of categories, and M the input dimension. It is possible, however, to formulate ART in a non-recursive algorithm such that the complexity is of order O(MN) only.
引用
收藏
页码：37 / 41
页数：5
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