This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input...

Traditionaly, fuzzy logic has been viewed in the AI community as an approach for managing uncertainty. In the 1990's, however, fuzzy logic has emerged as a paradigm for approximating a functional mapping. This complementary mordern view about the technology offers new insights about the foundation of fuzzy logic as well as new challenges regarding the identification of fuzzy models. In this pap...

Fuzzy regression discontinuity (RD) design and instrumental variable(s) (IV) regression share similar identification strategies and numerically yield the same results under certain conditions. While the weak identification problem is widely recognized in IV regressions, it has drawn much less attention in fuzzy RD designs, where the standard t-test can also suffer from asymptotic size distortio...

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of “If..., then...” statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environm...

For the characteristics that a hypersonic vehicle has a large span of flight height and flight Mach number, and complicated flight environment, the model of which is highly nonlinear, unstable and multivariablecoupled, where using a single modelling approach is often difficult to achieve high modeling accuracy, hybrid modeling method is proposed to design its performance digital mock-up. The pe...

In the cases of control systems where physical mechanisms are not well known due to high complexity and nonlinearity, a fuzzy relational model may be useful. In this paper, we propose a recursive parameter tuning algorithm for the identification of fuzzy relational model for an unknown dynamic system. Furthermore, using the fact that a dynamic system is represented by the relational strength be...

This paper presents an approach to building multi-input and single-output fuzzy models. Such a model is composed of fuzzy implications, and its output is inferred by simplified reasoning. The implications are automatically generated by the structure and parameter identification. In structure identification, the optimal or near optimal number of fuzzy implications is determined in view of valid ...

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-Kang (TSK) model, where the consequence part of each fuzzy rule is a linear equation. Structure identification is performed by a fuzzy adaptive resonance theory (ART)-like mechanism, assisted by fuzzy rule splitting and adding...

AbstractThis paper defines an intelligent decision support system for diagnostics for typhoid fever. Typhoid fever has numerous variables involved in identification of the disease. These variable are transformed into membership functions and passed onto the Fuzzy Logic System. The fuzzy logic System consists of the following stages fuzzification which converts the crisp data into variable data,...