ation Accuracy Analys s as Function Approximators
Xiao‐Jun Zeng, M.G. Singh · 1996
This paper establishes the approximation error bounds for various classes of fuzzy systems (Le., fuzzy systems generated by different inferential and defuzzification methods). Based on these bounds, the approximation accuracy of various classes of fuzzy systems is analyzed and compared. It is seen that the class of fuzzy systems generated by the product inference and the center-average defuzzifier has better approximation accuracy and properties than the class of fuzzy systems generated by the min inference and the center-average defuzzifer, and the class of fuzzy systems defuzzified by the MOM defuzziiier. In addition, it is proved that fuzzy systems can represent any linear and multilinear function and explicit expressions of fuzzy systems generated by the MQM defuzzified method are given.