A Fuzzy Relational Identification Algorithm and Its Application to Predict The Behaviour of a Motor Drive System
Abstract
Fuzzy relational identification builds a relational model describing systems behaviour by a nonlinear mapping between its variables. In this paper, we propose a new fuzzy relational algorithm based on simplified maxmin relational equation. The algorithm presents an adaptation method applied to gravitycenter of each fuzzy set based on error integral value between measured and predicted system output, and uses the concept of timevariant universe of discourses. The identification algorithm also includes a method to attenuate noise influence in extracted system relational model using a fuzzy filtering mechanism. The algorithm is applied to onestep forward prediction of a simulated and experimental motor drive system. The identified model has its inputoutput variables (statorreference current and motor speed signal) treated as fuzzy sets, whereas the relations existing between them are described by means of a matrix R defining the relational model extracted by the algorithm. The results show the good potentialities of the algorithm in predict the behaviour of the system and attenuate through the fuzzy filtering method possible noise distortions in the relational model.
 Publication:

arXiv eprints
 Pub Date:
 September 2000
 arXiv:
 arXiv:cs/0010004
 Bibcode:
 2000cs.......10004C
 Keywords:

 Computer Science  Robotics;
 Computer Science  Machine Learning;
 I.2.9;
 I.2.6
 EPrint:
 12 pages