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, YevgeniyViktorov, Yevgen Keywords: Artificial Neural NetworksConstructive ApproachFuzzy InferenceHybrid SystemsNeo-Fuzzy NeuronReal-Time ProcessingOnline 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

Files in This Item:

File Description SizeFormat