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The authors propose algorithm for the automatic collision avoidance system using fuzzy inference in this paper.
To determine the degree of deprivation, we then develop a new system using fuzzy logic.
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.
This constructed model would facilitate the development of an expert, real-time and integrated system using fuzzy logic technology to monitor the level of the slurry erosion process.
A fuzzy-based FMEA is introduced to express such uncertainties appropriately and to provide flexible access to a risk picture for a new system using fuzzy modeling.
This research develops a balancing approach for a novel SAMS model, called the cart seesaw system, using fuzzy logic and fuzzy coordinator compensation to drive the sliding carts and keep the seesaw angle close to zero in the equilibrium state.
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The expert system uses fuzzy rules, which are structured with Data Engine.
As a theoretical framework the system uses fuzzy set theory and other soft computing techniques.
The system uses fuzzy logic to input and output information and to perform weighted inferencing.
The fuzzy inference system uses fuzzy concepts where uncertainties are inherently unavoidable due to insufficient information and engineering assumption and exploits fuzzy rules in the multi-criteria evaluation by replicating heuristic knowledge and experience in logic-based descriptive rules.
The proposed system uses fuzzy rule format defined in Equation (2), which offers a flexible structure to the rules, allowing each patient feature to take more than one value and facilitating the extraction of general UA risk assessment rules.
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