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A soft multi-criteria decision-making approach to assessing the goodness of typical reasoning systems based on empirical data

机译：基于经验数据的软多准则决策方法，用于评估典型推理系统的优劣

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摘要

A soft multi-criteria decision-making system for the assessment of typical decision algorithms applying empiric data was constructed. The evaluation criteria were simplicity, correspondence with human reasoning, correspondence with reality, content validity and residual distribution. By virtue of our soft computing (SC) approach, we were able to computerize the assessments. The decision-making system was used to examine the Zimmermann-Zysno data of tiles by applying both conventional and those based on SC decision algorithms. The conventional algorithms comprised linear and non-linear regression analysis, whereas a fuzzy and a neuro-fuzzy system represented the SC approach. The SC approach seemed to yield better results within the bounds of the given criteria.
机译：构造了一个软多准则决策系统，用于评估采用经验数据的典型决策算法。评价标准为简单，符合人类推理，符合现实，内容效度和残差分布。凭借我们的软计算（SC）方法，我们能够将评估计算机化。该决策系统通过应用常规决策和基于SC决策算法的数据来检查图块的Zimmermann-Zysno数据。常规算法包括线性和非线性回归分析，而模糊和神经模糊系统代表了SC方法。在给定标准的范围内，SC方法似乎产生了更好的结果。

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