Results 161 to 170 of about 24,496 (212)

Takagi-Sugeno Fuzzy Models

2010
In this chapter we first introduce the continuous-time Takagi-Sugeno (TS) fuzzy systems that are employed throughout the book. In the second part of the chapter, we present methods to construct TS models that represent or approximate a nonlinear dynamic system starting from a given model of this system.
Zsófia Lendek, Thierry Marie Guerra, Robert Babuška, Bart De Schutter   +3 more
openaire   +1 more source

Output stabilization of Takagi–Sugeno fuzzy systems

Fuzzy Sets and Systems, 2000
The authors discuss a class of Takagi-Sugeno models governed by rules of the form \[ \text{-if } z_1\text{ is }M_{1i} \text{ and\dots and }z_p \text{ is }M_{pi} \text{ then }dx/dt= A_ix(t)+B_i u(t),\;y(t)=C_ix(t) \] (in the above \(x(t)\in \mathbb{R}^n\), \(u(t)\in \mathbb{R}^m\) and \(y(t)\in \mathbb{R}^q\), \(i=1, 2, \dots,r\); \(M_{1i},\dots,M_{pi}\)
Yoneyama, Jun, Nishikawa, Masahiro, Katayama, Hitoshi, Ichikawa, Akira   +3 more
openaire   +2 more sources

Takagi–Sugeno Fuzzy Modeling Using Mixed Fuzzy Clustering

IEEE Transactions on Fuzzy Systems, 2017
This paper proposes the use of mixed fuzzy clustering (MFC) algorithm to derive Takagi–Sugeno (T–S) fuzzy models (FMs). Mixed fuzzy clustering handles both time invariant and multivariate time variant features, allowing the user to control the weight of each component in the clustering process. Two model designs based on MFC are investigated.
Catia M. Salgado, Joaquim L. Viegas, Carlos S. Azevedo, Marta C. Ferreira, Susana M. Vieira, Joao M. C. Sousa   +5 more
openaire   +1 more source

Constrained fuzzy controller design of discrete Takagi–Sugeno fuzzy models

Fuzzy Sets and Systems, 2003
The contribution of this paper is to provide a method in the design of a discrete fuzzy controller with linear output feedback gains for the discrete-time Takagi-Sugeno fuzzy systems. This method is based on the concept of the PDC (parallel distributed compensation) and it is developed according to a specified common observability Gramian, which can ...
Chang, Wen-Jer, Sun, Chein-Chung
openaire   +1 more source

ON FUZZINESS MEASURES VIA SUGENO'S INTEGRAL

1995
We introduce a notion of partial order for fuzzy sets in connection with their greater or smaller fuzziness. This allows us to give a simple basis to theory of fuzziness measures. Sugeno's integral permits to built very large classes of fuzziness measures.
P. BENVENUTI, VIVONA, Doretta, M. DIVARI
openaire   +2 more sources

Approximations of large rule Takagi-Sugeno fuzzy controller by four rule Takagi-Sugeno fuzzy controller

IEEE Conference on Cybernetics and Intelligent Systems, 2004., 2004
This paper deals with approximations of large rule Takagi-Sugeno (TKS) fuzzy controller by four rule TKS fuzzy controller (simpler) via comparison of rule base of two controllers. The inequalities between two controllers are compensated by proposed compensating factors.
R.K. Arya, R. Mitra, V. Kumar
openaire   +1 more source

Sensitivity analysis of Takagi–Sugeno fuzzy neural network

Information Sciences, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Jian, Chang, Qin, Gao, Tao, Zhang, Kai, Pal, Nikhil R.   +4 more
openaire   +2 more sources

Takagi-Sugeno Fuzzy Logic Systems

2017
The achievements obtained by Fuzzy Logic undoubtedly changed the way expert information is represented, manipulated, and interpreted in computational systems. Nevertheless, the initialization of Mamdani FLSs’ main parameters, namely its membership functions and their interdependency relations, is a process that depends on the knowledge of an expert ...
Rómulo Antão, Alexandre Mota, Rui Escadas Martins, José Tenreiro Machado   +3 more
openaire   +1 more source

A Takagi-Sugeno fuzzy gain-scheduler

Proceedings of IEEE 5th International Fuzzy Systems, 2002
In the present paper we describe the design of a fuzzy gain scheduler for tracking a reference trajectory of a nonlinear autonomous system. The proposed fuzzy gain scheduling method has two major advantages over the existing crisp gain scheduling methods.
D. Driankov, R. Palm, U. Rehfuess
openaire   +1 more source

Improved Takagi-Sugeno fuzzy approach

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), 2008
In this paper Takagi-Sugeno fuzzy approach in analyzed under the fuzzy mapping perspective. Although similar to classical Takagi-Sugeno fuzzy approach in structure, this rule based system differs when employing a subnormal fuzzy mapping instead of using a normalized one.
openaire   +1 more source