Control problems in the industry are dominated by nonlinear and time-varying behavior, many sensors that measure all kinds of variables, many loops and interaction among the control loops. The extraction of (fuzzy) information out of raw data is very important and contains saving potential for industrial applications. Major types of rule-based fuzzy models are described which are based on pre-p...

A new systematic algorithm to build adaptive linguistic fuzzy models directly from input-output data is presented in this paper. Based on clustering and projection in the input and output spaces, significant inputs are selected, the number of clusters is determined, rules are generated automatically, and a linguistic fuzzy model is constructed. Then, using a simplified fuzzy reasoning mechanism...

the functions employed in an estimation of costly measured soil properties from either widely available or more easily obtained basic soil properties are referred to as pedotransfer functions. to develop pedotransfer functions, one can use multivariate regression, neural networks and neuro-fuzzy models. to make a comparison among the mentioned models, 153 soil samples were collected from soils ...

This paper proposes to use two information retrieval system models (Boolean information retrieval model and extended Boolean (fuzzy) information retrieval model). These models differ by using Boolean queries or fuzzy weighted queries. It also proposes a way for optimizing user query for the two models by using genetic programming and fuzzy logic. And proposes to use more number of Boolean opera...

The paper offers evolving Takagi–Sugeno (T–S) fuzzy models for a nonlinear benchmark represented by the pendulum-crane system and focused on the dynamics of pendulum angular position behavior. The rule bases and parameters of T–S fuzzy models are continuously evolved by an online identification algorithm in terms of computing the potentials of new data points. Accepting the pendulum angle as mo...

Models based on neural and neuro-fuzzy structures are developed to represent knowledge about a large data set containing chemical descriptors of organic compounds, commonly used in industrial processes. The neuro-fuzzy models here proposed include both, QSARs and original numerical values. The developed approaches use various techniques to insert knowledge by training, and to map rules in neuro...

The approach of continuous evaluation is an important methodology in educational learning process. However, only recently it was applied in training based on virtual reality. This paper presents a methodology of evaluation that uses a fuzzy continuous evaluation approach to provide a user profile from his several training. This information can be used to improve the user performance in the real...

In this paper, the notion of a fuzzy vector of normalized weights is introduced, and its application in decision making models based on the weighted average operation is studied. The fuzzy vector of normalized weights represents a generalization of normalized fuzzy weights that authors formerly applied for modeling uncertain weights of criteria and/or uncertain probabilities of states of the wo...

radial models of dea to measure the efficiency of dmus assumed an inefficient unit to be efficient must decrease (increase) proportional all inputs (outputs) as the same. this assumption is absolutely false, unnecessary and unrealistic, therefore, to address this deficiency, and raise awareness of how to use resources, it is so realistic that expect the various inputs have different efficiency....