This text provides an introduction to the use of fuzzy sets and fuzzy logic for the approximation of functions and modeling of static and dynamic systems. The concept of a fuzzy system is first explained. Afterwards, the motivation and practical relevance of fuzzy modeling are highlighted. Two types of rule-based fuzzy models are described: the linguistic (Mamdani) model and the Takagi–Sugeno m...

to use soil hydrology processes (shp) models, which have increasingly extended during the last years, comprehensive knowledge about these models and their modeling approaches seems to be necessary. the modeling approaches can be categorized as either classical or non-classical. classical approaches mainly model the shp through solving the general unsaturated flow (richards) equation, numericall...

in recent years, use of fuzzy collection theories for modeling of hydrological phenomenon's that is including complexity and uncertainly is considered scholars. so in this research, adaptive neuro-fuzzy inference system (anfis) is used for performance of river flow forecasting process. in this research, three parameters such as raining, temperature and daily discharge of lighvanchai basin ...

introduction drastic model is an index and overlapping model that has been designed for producing vulnerability scores for different points by combining several thematic layers. overlapping distinguished methods are themost applicable methods for evaluatingvulnerability of aquifers because they are cheap, they can directly reach a defined goal, the used data in the methods are accessible or can...

nowadays, due to the competitive conditions of global market, corporations try to outsource their extraneous processes to third-party suppliers. so, selecting a proper supplier play a significant role in organization success. the supplier selection problem can be viewed as a group decision-making problem with multiple criteria. since in previous researches the inter relationship between criteri...

In the recent years, a high number of fuzzy rule learning algorithms have been developed with the aim of building the Knowledge Base of Linguistic Fuzzy Rule Based Systems. In this context, it emerges the necessity of managing a flexible structure of the Knowledge Base with the aim of modeling the problems with a higher precision. In this work, we present a short overview on the Hierarchical Fu...

This paper proposes fuzzy modeling using obtained data. Fuzzy system is known as knowledge-based or rule-bases system. The most important part of fuzzy system is rule-base. One of problems of generation of fuzzy rule with training data is inconsistence data. Existence of inconsistence and uncertain states in training data causes high error in modeling. Here, Probability fuzzy system presents to...

E-learning model is examined of three major dimensions. And each dimension has a range of indicators that is effective in optimization and modeling, in many optimization problems in the modeling, target function or constraints may change over time that as a result optimization of these problems can also be changed. If any of these undetermined events be considered in the optimization process, t...

One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the propo...