In this paper, a new architecture combining dynamic neural units and fuzzy logic approaches is proposed for a complex chemical process modeling. Such processes need a particular care where the designer constructs the neural network, the fuzzy and the fuzzy neural network models which are very useful in black box modeling. The proposed architecture is specified to the pH chemical reactor due to ...

Neuro-Fuzzy Modeling has been applied in a wide variety of fields such as Decision Making, Engineering and Management Sciences etc. In particular, applications of this Modeling technique in Decision Making by involving complex Systems of Linear Algebraic Equations have remarkable significance. In this Paper, we present Polak-Ribiere Conjugate Gradient based Neural Network with Fuzzy rules to so...

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 an adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure ...

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 ...

Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with crisp spatial objects, that is, objects whose extent, shape, and boundary are precisely determined. Examples are land parcels, school districts, and state territories. However, many new, emerging applications are interested in modeling and processing geograph...

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...