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Enhanced fuzzy system for student's academic evaluation using linguistic hedges

机译:使用语言对冲学生学习学术评估增强模糊系统

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In this study, the effect of concentration, intensification and dilation of three common linguistic hedges (LHs), namely, very, indeed, and more or less on the performance of a fuzzy system for evaluating student's academic evaluation is presented. A LH may be viewed as an operator that acts on a fuzzy set representing the meaning of its operand. As an example, the operator very acts on the fuzzy meaning of the term high grade to have a secondary meaning of very high grade. This property changes the shape of the fuzzy sets and hence the amount of overlap between adjacent sets. It, in turn, improves the meaning of the fuzzy rules and hence the accuracy of the proposed fuzzy evaluation systems. The proposed LHs based fuzzy evaluator systems are compared with a standard fuzzy sets based fuzzy evaluator system using an example drawn from literature. Empirical results of the example presented in this paper show that concentration and dilation effect of LHs is not significant compared to standard fuzzy sets.
机译:在这项研究中,提出了三种常见语言篱笆(LHS)的浓度,强化和扩张的影响,即非常实际上,或多或少地对评估学生的学术评估的模糊系统的性能。 LH可以被视为操作员,该操作员在代表其操作数的含义上的模糊集上起作用。作为示例,操作员非常采用术语高级的模糊含义,以具有非常高等级的二次含义。此属性更改模糊集的形状,从而改变相邻集之间的重叠量。反过来,它提高了模糊规则的含义,从而提高了所提出的模糊评估系统的准确性。基于LHS的基于LHS的模糊评估器系统与使用从文献中汲取的示例的示例的基于标准模糊组的模糊评估器系统进行了比较。本文介绍的实施例的经验结果表明,与标准模糊集合相比,LHS的浓度和扩张效果并不重要。

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