DocumentCode :
2736179
Title :
Defuzzication using polynomial approximation
Author :
Mustafa, M. Marzuki
Author_Institution :
Fac. of Eng., Univ. Kebangsaan Malaysia, Selangor, Malaysia
Volume :
3
fYear :
2000
fDate :
2000
Firstpage :
342
Abstract :
A new fuzzy inference based on piecewise polynomial interpolation similar to spline technique is proposed. The signed membership function which can encode more information than the usual membership function is also introduced and used together with this new inference method. The fuzzy system using this inference method is more compact compared to other types of fuzzy systems. Online recursive training algorithm is also proposed to tune these polynomials if numerical training data is available. In contrast to neural network where the trained network is only a function of training data, both the heuristic prior knowledge and available training data are used. A simulation example is given to show how this new fuzzy inference can be applied in model reference closed-loop control system
Keywords :
closed loop systems; fuzzy logic; fuzzy set theory; fuzzy systems; inference mechanisms; interpolation; learning (artificial intelligence); closed-loop control; fuzzy inference; fuzzy system; membership function; piecewise polynomial interpolation; polynomial approximation; recursive training; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Inference algorithms; Input variables; Interpolation; Neural networks; Polynomials; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
TENCON 2000. Proceedings
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6355-8
Type :
conf
DOI :
10.1109/TENCON.2000.892286
Filename :
892286
Link To Document :
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