Intuitionistic Fuzzy Inference System with Genetic Tuning for Predicting Financial Performance

P. Hájek, V. Olej
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引用次数: 1

Abstract

Intuitionistic fuzzy inference systems are used to model the uncertainty associated with positive and negative information and preferences. Here, we propose a novel intuitionistic fuzzy inference system of the Takagi-Sugeno-Kang type with genetic tuning. A genetic fuzzy apriori algorithm is used to obtain both the set of if-then rules and the initial values of the premise parameters. Then, a genetic algorithm is applied to tune the premise and consequent parameters of the intuitionistic fuzzy inference system. We demonstrate the effectiveness of the proposed system for predicting corporate financial performance and show that the system has higher prediction accuracy than state-of-the-art fuzzy inference systems.
基于遗传调谐的财务绩效预测直觉模糊推理系统
直觉模糊推理系统用于模拟与正、负信息和偏好相关的不确定性。在此,我们提出了一种新的带有遗传调谐的Takagi-Sugeno-Kang型直觉模糊推理系统。利用遗传模糊先验算法获得了假设-然后规则集和前提参数的初始值。然后,应用遗传算法对直觉模糊推理系统的前提参数和结果参数进行调整。我们证明了所提出的系统在预测公司财务绩效方面的有效性,并表明该系统比最先进的模糊推理系统具有更高的预测精度。
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