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- W2069251669 abstract "In this paper, the learning process of ART 2 (adaptive resonant theory) network is applied to construct the structure of cerebellar model articulation controller (CMAC) to form an ART-type CMAC network. The proposed updating rule is in an unsupervised manner as the ART 2 network or the self-organizing map (SOM), and could equally distribute the learning information into the association memory locations as the CMAC network. If the winner fails a vigilance test, a new state is created; otherwise, the memory contents corresponding to the winner are updated according to the learning information. Like SOM, the proposed network also has a neighborhood region, but the neighborhood region is implicit in the network structure and need not be defined in advance. This paper also analyzes the convergence properties of the ART-type CMAC network. The proposed network is applied to solve data classification problems for illustration. Experiment results demonstrate the effectiveness and feasibility of the ART-type CMAC network in solving five benchmark datasets selected from the UCI repository." @default.
- W2069251669 created "2016-06-24" @default.
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- W2069251669 date "2011-02-01" @default.
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- W2069251669 title "ART-type CMAC network classifier" @default.
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- W2069251669 doi "https://doi.org/10.1016/j.neucom.2010.10.003" @default.
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