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Book 09 Intelligent Processing >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1218

Title: The Cascade Neo-Fuzzy Architecture and its Online Learning Algorithm
Authors: Bodyanskiy, Yevgeniy
Viktorov, Yevgen
Keywords: Artificial Neural Networks
Constructive Approach
Fuzzy Inference
Hybrid Systems
Neo-Fuzzy Neuron
Real-Time Processing
Online Learning
Issue Date: 2009
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode.
URI: http://hdl.handle.net/10525/1218
ISSN: 1313-0455
Appears in Collections:Book 09 Intelligent Processing

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