The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Fuzzy min-max neural networks. I. Classification
Abstract
A supervised learning neural network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregate (union) of fuzzy set hyperboxes. A fuzzy set hyperbox is an n-dimensional box defined by a min point and a max point with a corresponding membership function. The min-max points are determined using the fuzzy min-max learning algorithm, an expansion-contraction process that can learn nonlinear class boundaries in a single pass through the data and provides the ability to incorporate new and refine existing classes without retraining. The use of a fuzzy set approach to pattern classification inherently provides a degree of membership information that is extremely useful in higher-level decision making. The relationship between fuzzy sets and pattern classification is described. The fuzzy min-max classifier neural network implementation is explained, the learning and recall algorithms are outlined, and several examples of operation demonstrate the strong qualities of this new neural network classifier.
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Computational Intelligence: An Introduction
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A Study of an EMG-controlled HCI Method by Clenching Teeth
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Metadata
- Authors
- Simpson, P. K.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1992
- Volume
- 3
- Issue
- 5
- Pages
- 776-786
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2007187232
- Cited by
- 782
- References
- 41
- FWCI
- 12.97
- Type
- journal article
- Language
- en
- Open access
- closed
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