The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK
model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear …

We develop a neurofuzzy network technique to extract TSK-type fuzzy rules from a given set
of input-output data for system modeling problems. Fuzzy clusters are generated …

This paper proposes a new genetic-based modeling method for building simple and well-
defined TSK models with scatter-type input partitions. Our approach manages all attributes …

In order to overcome the difficulty in Takagi-Sugeno-Kang (TSK) fuzzy modeling for large
datasets, scalable TSK (STSK) fuzzy-model training is investigated in this study based on …

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 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 …

Theoretical studies have shown that fuzzy models are capable of approximating any
continuous function on a compact domain to any degree of accuracy. However, constructing …

NeuroFAST is an on-line fuzzy modeling learning algorithm, featuring high function
approximation accuracy and fast convergence. It is based on a first-order Takagi-Sugeno …

This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can
express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's …

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 …