Fuzzy rule base design using tabu search algorithm for nonlinear system modeling

T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm

“…1 9 Xu and Lu (1987) 2 0.328 Pedrycz (1984) 2 0.321 Chen et al (1998) 2 0.268 Kroll (1996) 2 0 . 1 4 9 Evsukoff et al (2002) 2 0.090 Kim et al (1997) 6 0.062 Kim et al (1998a) 6 0.055 Chiu (1996) 3 0.072 Tsekouras (2005) 6 0.075 Kung and Su (2007) 2 0.0518 Bagis (2008) 4 0 . 1 4 8 Our model 4 0.0498 Table 5 List of the parameters of our model in the Box and Jenkins example.…”

“…In order to perform structure identification and parameter estimation, many methods were used in literature. Adaptive schemes were adopted in Jang (1992), heuristic approaches appeared in Nozaki et al (1997) and Bagis (2008), nearest neighbor clustering were utilized in Wang et al (2002) and Chiang and Hao (2004) showed that support vector-learning mechanisms could achieve good performance in fuzzy modeling applications. Besides, fuzzy clustering algorithms are widely used in fuzzy modeling.…”

A new T–S fuzzy-modeling approach to identify a boiler–turbine system

“…Number of rules MSE Box and Jenkins (1970) 6 0.202 Tong (1980) 2 0.469 Sugeno and Yasukawa (1993) 3 0.19 Xu and Lu (1987) 2 0.328 Pedrycz (1984) 2 0.321 Chen, Xi, and Zhang (1998) 2 0.268 Kroll (1996) 2 0.149 Evsukoff, Branco, and Galichet (2002) 2 0.090 Kim et al (1997) 6 0.062 6 0.055 Chiu (1996) 3 0.072 Tsekouras (2005) 6 0.075 Bagis (2008) 4 0.148 Our model 3 0.056 for turbine, u 3 is level of feedwater regulator, y 3 is level of drum (m), a cs is coefficient for the quality of steam, a cs ¼ ð1 À 0:001538x 3 Þð0:8x 1 À 25:6Þ x 3 ð1:0394 À 0:0012304x 1 Þ ;…”

“…The most important task to accomplish a fuzzy model is to perform structure identification, which is concerned with the determination of the number of rules and parameter estimation. In order to perform structure identification and parameter estimation, many methods were used in literature, including heuristic approaches, support vector-learning mechanisms, and so on (Bagis, 2008;Chiang & Hao, 2004;Wang, Rong, & Wang, 2002). Besides, fuzzy-clustering algorithms are widely used approaches in fuzzy modeling, for which they have good ability to partition fuzzy space without supervision, providing foundation for extraction of fuzzy rules.…”

An adaptive fuzzy logic controller based on real coded quantum-inspired evolutionary algorithm

“…Meredith [3] also applies GA to the fine tuning of MFs in a FLC for a helicopter. Bagis [4] have presented an approach for the determination of the structure and parameters of fuzzy RB. He applied this approach in the modeling of the nonlinear or complex systems.…”