文章基本信息

标题：Neo-Fuzzy Encoder and Its Adaptive Learning for Big Data Processing
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作者：Yevgeniy Bodyanskiy ; Iryna Pliss ; Olena Vynokurova 等
期刊名称：Information Technology and Management Science
印刷版ISSN：2255-9086
电子版ISSN：2255-9094
出版年度：2017
卷号：20
期号：1
页码：6-11
DOI：10.1515/itms-2017-0001
语种：English
出版社：Walter de Gruyter GmbH
摘要：In the paper a two-layer encoder is proposed. The nodes of encoder under consideration are neo-fuzzy neurons, which are characterised by high speed of learning process and effective approximation properties. The proposed architecture of neo-fuzzy encoder has a two-layer bottle neck” structure and its learning algorithm is based on error backpropagation. The learning algorithm is characterised by a high rate of convergence because the output signals of encoder’s nodes (neo-fuzzy neurons) are linearly dependent on the tuning parameters. The proposed learning algorithm can tune both the synaptic weights and centres of membership functions. Thus, in the paper the hybrid neo-fuzzy system-encoder is proposed that has essential advantages over conventional neurocompressors.
关键词：Artificial neural networks ; computational intelligence ; data compression ; machine learning