Some novel approaches to estimate Nonlinear Output Error (NOE) models using TS fuzzy models for
a class of nonlinear dynamic systems having variability in their outputs is presented in this
dissertation. Instead of using unrealistic assumptions about uncertainty the most common of
which is normality the proposed methodology tends to capture effects caused by the real
uncertainty observed in the data. The methodology requires that the identification method must
be repeated offline a number of times under similar conditions. This leads to multiple
inputoutput time series from the underlying system. These time series are preprocessed using
the techniques of statistics and probability theory to generate the envelopes of response at
each time instant. By incorporating interval data in fuzzy modelling and using the theory of
symbolic interval-valued data a TS fuzzy model with interval antecedent and consequent
parameters is obtained. The proposed identification algorithm provides for a model for
predicting the center-valued response as well as envelopes as the measure of uncertainty in
system output.
