Developmental Resonance Network

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Adaptive resonance theory (ART) networks deal with normalized input data only, which means that they need the normalization process for the raw input data, under the assumption that the upper and lower bounds of the input data are known in advance. Without such an assumption, ART networks cannot be utilized. To solve this problem and improve the learning performance, inspired by the ART networks, we propose a developmental resonance network (DRN) by employing new techniques of a global weight and node connection and grouping processes. The proposed DRN learns the global weight converging to the unknown range of the input data and properly clusters by grouping similar nodes into one. These techniques enable DRN to learn the raw input data without the normalization process while retaining the stability, plasticity, and memory usage efficiency without node proliferation. Simulation results verify that our DRN, applied to the unsupervised clustering problem, can cluster raw data properly without a prior normalization process.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-04
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.30, no.4, pp.1278 - 1284

ISSN
2162-237X
DOI
10.1109/TNNLS.2018.2863738
URI
http://hdl.handle.net/10203/253933
Appears in Collection
EE-Journal Papers(저널논문)
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