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In technical applications, fuzzy control refers to programs or algorithms using fuzzy logic to allow machines to make decisions based on the practical knowledge of a human operator.
This paper describes the approach of using fuzzy logic to design a measurement system in outcome-based education (OBE).
The objective of this research was to design a temperature control system using fuzzy logic to meet the optimal condition for mushroom cultivation in growth chambers.
We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results.
Also, the operators of all stages of simulated evolution have been implemented using fuzzy logic to exploit the nature of fuzzy information of the problem domain.
One of the speakers at the event, the computer scientist and I.B.M. Fellow Ron Fagin, described using fuzzy logic to solve the problem of combining information from two types of databases; the result was a very efficient, and very short (ten-line), algorithm.
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Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes.
The system uses fuzzy logic to input and output information and to perform weighted inferencing.
It was used fuzzy logic to assess uncertainties in the system.
This paper presents a method that allows to use fuzzy logic to generate the local wind conditions caused by these geographic elements.
The purpose of this paper is to report on our preliminary two- and three-dimensional stratigraphic simulations that use fuzzy logic to model sediment production, sediment erosion, sediment transport and sediment deposition.
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