Evolving takagi sugeno modelling with memory for slow processes

Evolving takagi sugeno modelling with memory for slow processes

Evolving Takagi Sugeno (eTS) models are optimised for use in applications with high sampling rates. This mode of use produces excellent prediction results very quickly and with low memory requirements, even with large numbers of input attributes. In this paper eTS modelling is adapted for optimality in situations where memory usage and processing time are not specific requirements. The new meth...

Non-Linear System State Analysis via Takagi-Sugeno Fuzzy Modelling

Abstract: The fuzzy and neuro-fuzzy modeling approaches represent extremely powerful tool for non-linear dynamic systems approximation. By using this tool it is possible to overcome difficulties in conventional techniques for dealing with nonlinearity. This paper presents the design of the diagnostic system exploits these fuzzy modeling approximation abilities together with fault detection and ...

Observers for Takagi-Sugeno fuzzy systems

We focus on the analysis and design of two different sliding mode observers for dynamic Takagi-Sugeno (TS) fuzzy systems. A nonlinear system of this class is composed of multiple affine local linear models that are smoothly interpolated by weighting functions resulting from a fuzzy partitioning of the state space of a given nonlinear system subject to observation. The Takagi-Sugeno fuzzy system...

Takagi-Sugeno Fuzzy Modeling for Process Control

Observer Design for Takagi-Sugeno Descriptor System with Lipschitz Constraints

This paper investigates the design problem of observers for nonlinear descriptor systems described by Takagi-Sugeno (TS) system; Depending on the available knowledge on the premise variables two cases are considered. First a TS descriptor system with measurables premises variables are proposed. Second, an observer design which satisfying the Lipschitz condition is proposed when the premises var...