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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Analysis of the Noise Reduction Property of Type-2 Fuzzy Logic Systems Using a Novel Type-2 Membership Function
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Analysis of the Noise Reduction Property of Type-2 Fuzzy Logic Systems Using a Novel Type-2 Membership Function

机译：基于新型2类隶属度函数的2类模糊逻辑系统的降噪特性分析

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摘要

In this paper, the noise reduction property of type-2 fuzzy logic (FL) systems (FLSs) (T2FLSs) that use a novel type-2 fuzzy membership function is studied. The proposed type-2 membership function has certain values on both ends of the support and the kernel and some uncertain values for the other values of the support. The parameter tuning rules of a T2FLS that uses such a membership function are derived using the gradient descend learning algorithm. There exist a number of papers in the literature that claim that the performance of T2FLSs is better than type-1 FLSs under noisy conditions, and the claim is tried to be justified by simulation studies only for some specific systems. In this paper, a simpler T2FLS is considered with the novel membership function proposed in which the effect of input noise in the rule base is shown numerically in a general way. The proposed type-2 fuzzy neuro structure is tested on different input–output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts.
机译：本文研究了使用新型2型模糊隶属度函数的2型模糊逻辑（FL）系统（FLSs）（T2FLSs）的降噪特性。拟议的2类隶属度函数在支撑和内核的两端都具有某些值，而对于支撑的其他值则具有一些不确定的值。使用这种隶属函数的T2FLS的参数调整规则是使用梯度下降学习算法得出的。文献中有许多论文声称T2FLS在嘈杂条件下的性能优于1型FLS，并且仅通过对某些特定系统的仿真研究来证明这种说法是合理的。在本文中，考虑了一种更简单的T2FLS，并提出了新颖的隶属函数，其中以一般方式以数字方式显示了规则库中输入噪声的影响。在不同的输入-输出数据集上对提出的2型模糊神经结构进行了测试，结果表明，与1型对应物相比，具有新型隶属函数的T2FLS具有更好的降噪性能。

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