15,727 research outputs found
General fuzzy min-max neural network for clustering and classification
This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given
Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction
Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659
Data Editing for Neuro-Fuzzy Classifiers
In this paper we investigate the potential benefits and
limitations of various data editing procedures when
constructing neuro-fuzzy classifiers based on hyperbox
fuzzy sets. There are two major aspects of data editing
which we are attempting to exploit: a) removal of outliers
and noisy data; and b) reduction of training data size. We
show that successful training data editing can result in
constructing simpler classifiers (i.e. a classifier with a
smaller number and larger hyperboxes) with better
generalisation performance. However we also indicate
the potential dangers of overediting which can lead to
dropping the whole regions of a class and constructing
too simple classifiers not able to capture the class
boundaries with high enough accuracy. A more flexible
approach than the existing data editing techniques based
on estimating probabilities used to decide whether a
point should be removed from the training set has been
proposed. An analysis and graphical interpretations are
given for the synthetic, non-trivial, 2-dimensional
classification problems
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