The operation planning problem encounters several uncertainties in terms of the power system’s parameters such as load, operating reserve and wind power generation. The modeling of those uncertainties is an important issue in power system operation. The system operators can implement different approaches to manage these uncertainties such as stochastic and fuzzy methods. In this paper, new ...

System modeling with fuzzy rule-based systems (FRBSs), i.e. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to faithfully represent the real system. While linguistic FM (mainly developed by linguistic FRBSs) is focused o...

In this paper, we propose a new dynamical fuzzy modeling technique for SISO complex systems. It is seen that, the whole state-space of a complex system is divided into subspaces and a linear time-invariant (LTI) model is then used tu approximate the complex system in each subspace. However, the global dynamical fuzzy model of the complex system is derived by aggregating all subsystem poles and ...

In this paper, the computationally intelligent modeling approach for fluidized bed combustion process is considered, as well as intelligent process control based on developed models and soft computing methodologies. Applied genetic optimization and fuzzy modeling approach are combined with proposed intelligent control strategies. Efficient fuzzy nonlinear FBC process modeling by combining sever...

Modeling the suitability of land to support specific land uses is an important and common GIS application. Three classic models, specifically pass/ fail screening, graduated screening and weighted linear combination, are examined within a more general framework defined by fuzzy logic theory. The rationale underlying each model is explained using the concepts of fuzzy intersections, fuzzy unions...

A special method for system modeling is presented to develop multi-variable fuzzy models on the basis of the system’s measured input and output data. The global modeling problem is proposed to be solved in two steps: by fuzzy model configuration and fuzzy model identification. The fuzzy model configuration procedure is characterized by preliminary considerations leading to the definition of the...

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...

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy data modeling has been extensively investigated in various data models. Currently, huge amounts of electronic data are available on the Internet, and XML has been the de facto standard of information representation and exchange over the Web. This paper focuses on fuzzy XML data modeling, whic...

in this study, a multi-objective genetic algorithm (moga) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear multi-input multi-output (mimo) systems. in the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. furthermore, se- curing low-level and high-level ...

This paper focus several stability issues regarding the modeling of the dynamics of qualitative real world systems, and the ability of Fuzzy Cognitive Maps and Rule -Based Fuzzy Cognitive Maps to provide a faithful modeling in what concerns the stability properties of those systems. It also introduces the concept of Intrinsic Stability as a necessary property of qualitative system dynamics mode...