Title :
A hybrid fuzzy neural system as nonlinear system identifier
Author :
Stefanis, Eleftherios I. ; Theocharis, John ; Vachtsevanos, George
Author_Institution :
Dept. of Electr. & Comput. Eng., Thessaloniki Univ., Greece
Abstract :
A hybrid fuzzy neural architecture is proposed. The fuzzy neural system is a feedforward network that combines the basic notions of neural networks and fuzzy logic into a common structure. A back-propagation algorithm is used to train the FNS to perform the desired nonlinear mappings. Four simulation examples are presented where the fuzzy neural system is employed as a nonlinear identifier. Finally, comparisons between fuzzy neural systems, backpropagation neural networks and other fuzzy systems are given and discussed
Keywords :
backpropagation; feedforward neural nets; fuzzy neural nets; identification; neural net architecture; nonlinear systems; back-propagation algorithm; feedforward network; hybrid fuzzy neural system; nonlinear system identifier; Artificial intelligence; Computer architecture; Feedforward neural networks; Fuzzy neural networks; Fuzzy systems; Input variables; Instruction sets; Logic; Neural networks; Nonlinear systems;
Conference_Titel :
Industrial Automation and Control: Emerging Technologies, 1995., International IEEE/IAS Conference on
Conference_Location :
Taipei
Print_ISBN :
0-7803-2645-8
DOI :
10.1109/IACET.1995.527605
