Eg. Laukonen et Km. Passino, TRAINING FUZZY-SYSTEMS TO PERFORM ESTIMATION AND IDENTIFICATION, Engineering applications of artificial intelligence, 8(5), 1995, pp. 499-514
A fuzzy system can be constructed to interpolate between input-output
data to provide an approximation for the function that is implicitly d
efined by the input-output data-pair associations. This paper begins b
y explaining how function approximation techniques can be used to solv
e nonlinear estimation and system identification problems. Next, sever
al fundamental issues ave discussed, related to how to choose the inpu
t-output data pairs so that accurate function approximation. can be ac
hieved with fuzzy systems. Using this insight a technique called ''uni
form training'' is proposed, in which input sequences are chosen to pr
oduce good training data sets (''uniform training data sets''). Also,
a new technique for function approximation via fuzzy systems called ''
modified learning from examples'' is outlined, where membership functi
ons are specified and rules ave added to try to achieve a pre-specifie
d function approximation accuracy. Uniform training and the modified l
earning from examples technique are then illustrated on a simple pendu
lum example. In addition, the use of the modified learning from exampl
es approach is demonstrated in constructing a fuzzy system which can i
dentify actuator failures on an F-16 aircraft.