Title :
A neural approach to fuzzy modeling
Author_Institution :
Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
fDate :
29 June-1 July 1994
Abstract :
This paper is concerned with the problem of constructing a fuzzy model from numerical data through a self-organizing counter propagation network (SOCPN). Two self-organizing algorithms, unsupervised USOCPN and supervised SSOCPN, are introduced. SOCPN can be employed in two ways. It can be used as a knowledge extractor by which a set of rules are generated from the available numerical data set. The generated rule-base is then utilized by a fuzzy reasoning model. It can also be used as an online adaptive fuzzy model in which the rule-base in terms of connection weights is updated successively in response to the incoming measured data. The simulation results on some well studied examples are given.
Keywords :
fuzzy logic; inference mechanisms; knowledge acquisition; modelling; neural nets; uncertainty handling; fuzzy modeling; fuzzy reasoning; knowledge extractor; neural nets; numerical data set; online adaptive fuzzy model; rule-base; self-organizing counter propagation network; Bismuth; Data mining; Equations; Fuzzy control; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Marine vehicles; Numerical models; Pattern matching;
Conference_Titel :
American Control Conference, 1994
Print_ISBN :
0-7803-1783-1
DOI :
10.1109/ACC.1994.752454
