Effects of user selected conditions on modeling of dynamic systems using adaptive fuzzy model

Abstract

In this paper, major properties of an adaptive fuzzy model as a system identifier when trained by the back-propagation algorithm are discussed. The standard rule-based fuzzy models were used to identify discrete-time nonlinear dynamic systems. The method of selection of the input variables, the number of rules, and the learning rate are briefly discussed. Three methods for choosing the initial parameter of the fuzzy model are considered, namely the on-line, the off-line, and the random initial parameters. The implementation and the computational aspects of the training algorithm are also highlighted. Three examples of discrete-time nonlinear systems are used in the simulation study to show the effects of user selected conditions on the identification process. The results of the identification procedure show that they approximate the dynamic plants quite well. The correlation based model validity tests are used to validate the identified fuzzy model.