On interval-data based Type-1 Takagi-Sugeno fuzzy systems for uncertain nonlinear dynamic system identification

Abstract

A novel interval-data based Takagi-Sugeno fuzzy system is proposed to identify uncertain nonlinear dynamic systems by endowing the classical TS fuzzy system with probability theory and symbolic data analysis. Such systems have variability in their outputs, that is they produce varying responses each time when the same stimuli is applied to them under the same condition. Interval data is generated by repeating the identification experiment multiple times and applying the probabilistic techniques to get soft bounds of output. The interval data is then directly used in the TS fuzzy modelling, giving rise to interval antecedent and consequent parameters. This method does not require any specific assumption on the probability distribution of the random variable that models the uncertainty. The developed procedure is demonstrated for a pneumatic drive system.

On Optimal Test Signal Design and Parameter Identification Schemes for Dynamic Takagi-Sugeno Fuzzy Models Using the Fisher Information Matrix

“…The first criterion is the fraction of improvement by at least 25 % over the respective initial model's performance, which will be called ''success rate''. Figures 3,4,5,and 6 show the percentages of models reaching this threshold. Figures 3 and 4 show the results for the Narendra system, Figs.…”

“…Two independent goals, the reduction of the uncertainty and the improvement of the simulation quality on arbitrary validation data, are investigated. After a short introduction of the model class and the identification process, the method is presented followed by the presentation of three case studies, a simulated test system that has been used as a benchmark system before [3] as well as a test stand of a servo-pneumatic longitudinal drive (SPLD) that was used in Zaidi and Kroll [4], and a three-tank system to validate the results from the parameter study.…”