Hybrid fuzzy modeling of chemical processes

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

“…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.…”

Adaptive control scheme based on the least squares support vector machine network

“…In fact, the problem of controlling the pH process can be included in a variety of practical areas such as waste water treatment, biotechnology processing, and chemical processing (Henson and Seborg, 1994;Wang et al, 2002;Faanes and Skogestad, 2004). The difficulty in controlling the pH process arises mainly from its heavy nonlinearity and uncertainty.…”

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

“…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.…”