A neural network classifier enhanced by the fast-learning fuzzy ART mechanism is presented. The learning algorithm combines the fuzzy ART procedure for ...
A fuzzy-ART-enhanced neural classifier | IEEE Conference ...
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The experiments show that the proposed model can exhibit high-quality classification capability in either a continuous, discrete, linear or a nonlinear domain.
This paper focuses on the evolution of Fuzzy ARTMAP neural network classifiers, using genetic algorithms, with the objective of improving generalization ...
In this paper, the formulation of a new neural architecture is presented based on adaptive resonance theory (ART), for the pattern classification and image ...
This new model takes the competition and resonance method of the input nodes into the output nodes, putting the input nodes and output nodes competition and ...
Abstract—This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network ...
A neural network for classification problems with fuzzy inputs is proposed. A fuzzy input is represented as an LR-type fuzzy set.
FuzzyART (Fuzzy Adaptive Resonance Theory) is a machine learning method with analog inputs that was developed to learn from new events without forgetting ...
Missing: enhanced | Show results with:enhanced
The Fuzzy ARTMAP neural network consists of two. Fuzzy ART modules, designated as ART, and ARTI,, as well as an inter-ART module as shown in Figure 1. Inputs.
Missing: enhanced | Show results with:enhanced
May 28, 2006 · Experimental results have shown that compared with the original FART, this algorithm has a better segmentation and antinoise performance.