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.
Keywords: artificial neural networks, constructive approach, fuzzy inference, hybrid systems, neo-fuzzy neuron,
real-time processing, online learning.
ACM Classification Keywords: I.2.6 Learning – Connectionism and neural nets
THE CASCADE NEO-FUZZY ARCHITECTURE
AND ITS ONLINE LEARNING ALGORITHM
Yevgeniy Bodyanskiy, Yevgen Viktorov
http://foibg.com/ibs_isc/ibs-09/ibs-09-p14.pdf
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