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It can avoid any assumptions on statistical models by utilizing fuzzy rules to determine the magnitudes of α and β.
A T S fuzzy model is firstly formed by utilizing fuzzy theorem to amalgamate a set of linearized dynamic equations.
This paper presents a fuzzy chance-constrained scheduling model which utilizes fuzzy variables to describe uncertain features of distributed generators (DGs).
We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea.
This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks.
This technique utilizes fuzzy set theory to quantify uncertainties, fault tree to model the system, and lambda tau method to formulate the mathematical expressions of system's failure/repair rates and the genetic algorithm is utilized to solve established nonlinear programming problems.
Fuzzy goal programming utilizes fuzzy set theory to deal with a level of imprecision in the goal programming model.
Higher level analyses of DTGs utilized fuzzy k-means clustering as described previously [ 8, 55, 56].
Secondly, the error system is exactly converted into a Takagi-Sugeno fuzzy model utilizing sector nonlinearity approach.
The uncertain natures of the demands and price of final product are addressed in the proposed model utilizing stochastic theory and fuzzy sets.
The DEX model operated with linguistic statements derived from numerical values, while the fuzzy model utilized numeric input.
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