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dc.contributor.authorCarpenter, Gailen_US
dc.contributor.authorGrossberg, Stephenen_US
dc.contributor.authorRosen, Daviden_US
dc.date.accessioned2011-11-14T18:21:48Z
dc.date.available2011-11-14T18:21:48Z
dc.date.issued1991-06
dc.identifier.urihttps://hdl.handle.net/2144/2070
dc.description.abstractA Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.en_US
dc.description.sponsorshipBritish Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175)en_US
dc.language.isoen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-1991-015
dc.rightsCopyright 1991 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectFuzzy set theoryen_US
dc.subjectAdaptive Resonance Theory (ART)en_US
dc.subjectNeural networksen_US
dc.subjectPattern recognitionen_US
dc.subjectART 1en_US
dc.subjectCategorizationen_US
dc.subjectMemory searchen_US
dc.titleFuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance Systemen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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