One of the foci of the recent development in object-oriented modeling (OOM) has been the extension of OOM to fuzzy logic to capture and analyze informal requirements that are imprecise in nature. In this paper, a new approach to object-oriented modeling based on fuzzy logic is proposed to formulate imprecise requirements along four dimensions: (1) to extend a class by grouping objects with simi...

This paper studies the theory and modeling manner of solid oxide fuel cell (SOFC) into power network and its effect on small signal stability. The paper demonstrates the fundamental module, mathematical analysis and small signal modeling of the SOFC connected to single machine infinite bus (SMIB) system. The basic contribution of the study is to attenuate the low frequency oscillations by optim...

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 nonlinear system identification, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, securing low-level and high-level int...

In this paper, we present a complete design framework for an adaptive multiple agent fuzzy constraint-based controller (MAFCC) based on fuzzy penumbra constraint processing in each fuzzy constraint subnetwork collaborating with a connected constraint network and its corresponding semantic modeling in a rst-order predicate calculus (FOPC) language, with application to a complex hydraulic system....

A fuzzy-modeling method for the emulation of expert decision behavior or for static as well as dynamic systems is presented. The input – output dataset of the system – or expert behavior is changed using fuzzy-sets into examples in linguistic form. These resulting examples build the fundament of the machine learning process for rule production (ID3). The fuzzy sets are optimized in order to min...

This paper reviews motivations for introducing fuzzy sets and fuzzy logic to knowledge representation in artificial intelligence. First we consider some areas of successful application of conventional approaches to system analysis. We then discuss limitations of these approaches and the reasons behind these limitations. We introduce different levels of representation for complex systems and dis...

The aim of this paper is to address an emergent and important area of research, fuzzy control, by means of an inductive reasoning approach derived from the General System Theory, called Fuzzy Inductive Reasoning (FIR). This eld has had a growing interest and development in the last years, and has proved to be very useful for modeling and prediction of a large variety of systems from diierent ar...

We present a system capable of modeling human newspaper readers. It is based on the extraction of reported speech, which is subsequently converted into a fuzzy theory-based representation of single statements. A domain analysis then assigns statements to topics. A number of fuzzy set operators, including fuzzy belief revision, are applied to model different belief strategies. At the end, our sy...

introduction: kidney disease is a major public health challenge worldwide. epidemiologic data suggest a significant relationship between underlying diseases and decrease in glomerular filtration rate (gfr). clinical studies and laboratory research have shown that the mentioned parameter is effective in development and progression of the renal disease per se. in this study, we used learning-base...