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An investigation of membership functions on performance of ANFIS for solving classification problems

机译：征收核算问题案件核算问题的核算问题

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Adaptive neuro-fuzzy inference system (ANFIS) is one of the efficient machine learning techniques, which has been successfully employed in wide variety of applications. The performance of ANFIS depends on the selection of the number and shape of membership functions as these two factors influence the most on computational complexity and accuracy of the designed ANFIS-based model. Mostly, an expert knowledge is required in this regard. However, there is an immense need of an investigative study for helping researchers make better decision on the number and shape of membership functions for thier ANFIS models. Hence, this study examines the role of four popular shapes of membership functions on the performance of ANFIS while solving various classification problems. According to experiments, Gaussian membership function demonstrated higher degree of accuracy with lesser computational complexity as compared to the counterparts.
机译：自适应神经模糊推理系统（ANFIS）是有效的机器学习技术之一，它已在各种应用中成功使用。 ANFI的性能取决于隶属函数的数量和形状，因为这两个因素影响了最大的基于ANFIS的模型的计算复杂性和准确性。主要是，在这方面需要专业知识。然而，对帮助研究人员提出更好地决定Thier ANFIS模型的成员函数的数量和形状的调查研究。因此，本研究探讨了四种流行的成员功能在解决各种分类问题的同时对ANFI的性能的作用。根据实验，高斯成员函数与对应物相比，高斯隶属函数以较小的计算复杂性表现出更高的准确性。

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