Neurocomputations in Relational Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Relation between VGA-classifier and MLP: determination of network architecture
Fundamenta Informaticae - Special issue on soft computing
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Interval and ellipsoidal uncertainty models
Granular computing
Genetic granular classifiers in modeling software quality
Journal of Systems and Software
Pattern Recognition Letters
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Linguistic models and linguistic modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Granular clustering: a granular signature of data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy min-max neural networks -- Part 2: Clustering
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
Logic-oriented neural networks for fuzzy neurocomputing
Neurocomputing
A granular reflex fuzzy min-max neural network for classification
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this study, we introduce and discuss a category of genetically optimized fuzzy neural networks. As far as the underlying geometry of such networks is concerned, they are focused on revealing a hyperbox-based topology in numeric data. This class of the networks is developed around fuzzy tolerance neurons. Tolerance neurons form a generalized version of intervals (sets) arising in a form of fuzzy intervals. The architecture of the network reflects a hierarchy of geometric concepts typically exploited in data analysis: fuzzy intervals combined and-wise give rise to fuzzy hyperboxes and these in turn by being aggregated or-wise generate a summary of data as a collection of hyperboxes. We discuss a genetic form of optimization of the networks and provide an in-depth view into the geometry of the individual hyperboxes as well as the overall topology of the network. Numerical experiments deal with 2-D synthetic data.