Contributed articleFuzzy ART properties
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Cited by (71)
Monitoring roundness profiles based on an unsupervised neural network algorithm
2011, Computers and Industrial EngineeringCitation Excerpt :On the other hand, if ρ is chosen to be close to 1, many finely divided categories are formed. One fundamental option of ART models implemented in this work is fast learning (Huang et al., 1995): it enables the neural network to adapt quickly to the inputs. In this paper, the process to be monitored generates data where each sample consists of a roundness profile related to a circular or cylindrical part.
Learning in the feed-forward random neural network: A critical review
2011, Performance EvaluationCitation Excerpt :This criticism is less directed towards local neural network models, such as RBF-NNs [119], and ART neural networks [120]. In particular, for ART neural networks a number of results has appeared in the literature that attempts to explain how learning works in an ART neural network, and shed light on how this neural network produces its answers (see [121–126]). Furthermore, some attempts have been published in the literature to explain the answers that an MLP neural network produces, when it is confronted with classification problems (e.g., [127]), or understanding some of the limitations of gradient descent learning and the MLP (e.g., [128,129]).
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