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.
