Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when
they are identified from experimental data. It is shown that there exists a close relationship …

An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the
paper. It is based on a novel learning algorithm that recursively updates TS model structure …

The problem of identifying the parameters of the constituent local linear models of Takagi-
Sugeno fuzzy models is considered. In order to address the tradeoff between global model …

Fuzzy models, especially Takagi-Sugeno (TS) fuzzy models, have received particular
attention in the area of nonlinear modeling due to their capability to approximate any …

The goal of the local linear model trees (LOLIMOT) toolbox for MATLAB® is to provide the
industrial user and the research engineer with a fast and easy-to-use software package for …

During the past few years two principally different approaches to the design of fuzzy
controllers have emerged: heuristics-based design and model-based design. The main …

In this paper, we introduce a new algorithm for incremental learning of a specific form of
Takagi–Sugeno fuzzy systems proposed by Wang and Mendel in 1992. The new data …

The paper describes a method of fuzzy model generation using numerical data as a starting
point. The algorithm generates a Takagi–Sugeno–Kang fuzzy model, characterised with …

The paper describes an approach to generating optimal adaptive fuzzy neural models from
I/O data. This approach combines structure and parameter identification of Takagi-Sugeno …

A number of techniques have been introduced to construct fuzzy models from measured
data. Most attention has been focused on multiple-input, single-output (MISO) systems. This …