Please use this identifier to cite or link to this item:
標題: A fuzzified neural fuzzy inference network for handling both linguistic and numerical information simultaneously
作者: Juang, C.F.
Lee, C.I.
關鍵字: TSK-type fuzzy rules;fuzzy neural network;structure/parameter;learning;alpha-cut;linguistic information;truck backing control;systems;identification
Project: Neurocomputing
期刊/報告no:: Neurocomputing, Volume 71, Issue 1-3, Page(s) 342-352.
A fuzzified Takagi-Sugeno-Kang (TSK)-type neural fuzzy inference network (FTNFIN) that is capable of handling both linguistic and numerical information simultaneously is proposed in this paper. FTRNFN solves the disadvantages of most existing neural fuzzy systems which can only handle numerical information. The inputs and outputs of FTNFIN may be fuzzy numbers with any shapes or numerical values. Structurally, FTNFIN is a fuzzy network constructed from a series of fuzzy if-then rules with TSK-type consequent parts. The alpha-cut technique is used in input fuzzification and consequent part computation, which enables the network to simultaneously handle both numerical and linguistic information. There are no rules in FTNFIN initially since they are constructed on-line by concurrent structure and parameter learning. FTNFIN is characterized by small network size and high learning accuracy, and can be applied to linguistic information processing. The network has been applied to the learning of fuzzy if-then rules, a mathematical function with fuzzy inputs and outputs, and truck backing control problem. Good simulation results are achieved from all these applications. (C) 2007 Elsevier B.V. All rights reserved.
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2006.12.020
Appears in Collections:電機工程學系所

Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.