Fuzzy temporal sequence processing by fuzzified recurrent neural fuzzy network
Chia‐Feng Juang, Shiuan-Jiun Ku, Hao-Jung Huang · 2005
A fuzzified TSK-type recurrent neural fuzzy network (FTRNFN) for handling fuzzy temporal information is proposed in this paper. The inputs and outputs of FTRNFN are fuzzy patterns represented by Gaussian or isosceles triangular membership functions. In structure, FTRNFN is a recurrent fuzzy network constructed from a series of recurrent fuzzy if-then rules with TSK-type consequent parts. The recurrent property of FTRNFN enables it to deal with fuzzy patterns with temporal context. There are no rules in FTRNFN initially; they are constructed on-line by concurrent structure and parameter learning. The ability of TRFNFN is verified from a two-dimensional fuzzy temporal sequence prediction problem.