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Interval Type-2 Mutual Subsethood Fuzzy Neural Inference System (IT2MSFuNIS)
IEEE Transactions on Fuzzy Systems ( IF 11.9 ) Pub Date : 2018-02-01 , DOI: 10.1109/tfuzz.2016.2646750
Vuppuluri Sumati , C. Patvardhan

This paper presents an interval type-2 mutual subsethood fuzzy neural inference system (IT2MSFuNIS). A mutual subsethood measure between two interval type-2 fuzzy sets (IT2 FS) has been derived and has been used in determining the similarity between the IT2 FS inputs and IT2 FS antecedents. The consequent weights are taken to be interval sets. The inputs to the system are fuzzified into IT2 FSs with Gaussian primary membership function having fixed center and uncertain variance. Aggregation of type-2 mutual subsethood based activation spreads is performed using product operator. The output is obtained using simplified type-reduction followed by defuzzification. The system learns using memetic procedure involving differential evolution for global search and gradient descent for local exploitation in solution space. The mathematical modeling and empirical studies of IT2MSFuNIS bring forth its efficacy in problems pertaining to function approximation, time-series prediction, control, and classification. Comparisons with other type-1 and type-2 neuro-fuzzy systems verify that IT2MSFuNIS compares excellently with other models with a performance better than most of them both in terms of total number of trainable parameters and result accuracy. Empirical studies indicate the intelligent decision making capability of the proposed model. The main contribution of this paper lies in the identification of mutual subsethood to find out the correlation between IT2 FSs and to find out its applicability in diverse application domains. The improved performance of the proposed method can be attributed to the better contrast handling capacity of mutual subsethood method and uncertainty handling capacity of IT2 FSs. The integration of mutual subsethood with interval type-2 fuzzy logic puts forth a novel model with various merits as demonstrated amply with the help of well-known problems reported in the literature.

中文翻译:

区间类型 2 互子集模糊神经推理系统 (IT2MSFuNIS)

本文提出了一种区间类型 2 互子集模糊神经推理系统 (IT2MSFuNIS)。已经导出了两个区间类型 2 模糊集 (IT2 FS) 之间的相互子集度量,并已用于确定 IT2 FS 输入和 IT2 FS 前因之间的相似性。随后的权重被视为区间集。系统的输入被模糊化为具有固定中心和不确定方差的高斯主隶属函数的 IT2 FS。使用乘积运算符执行基于类型 2 相互子集的激活传播的聚合。输出是使用简化的类型归约和去模糊化获得的。该系统使用模因程序进行学习,该程序涉及全局搜索的差分进化和解决方案空间中局部开发的梯度下降。IT2MSFuNIS 的数学建模和实证研究在与函数逼近、时间序列预测、控制和分类有关的问题中发挥了作用。与其他类型 1 和类型 2 神经模糊系统的比较验证了 IT2MSFuNIS 与其他模型相比非常出色,在可训练参数的总数和结果准确性方面,其性能都优于大多数模型。实证研究表明所提出模型的智能决策能力。本文的主要贡献在于识别相互子集以找出 IT2 FS 之间的相关性并找出其在不同应用领域中的适用性。所提出方法的改进性能可归因于互子集方法更好的对比度处理能力和 IT2 FSs 的不确定性处理能力。相互子集与区间类型 2 模糊逻辑的集成提出了一个具有各种优点的新模型,正如在文献中报道的众所周知的问题的帮助下充分证明的那样。
更新日期:2018-02-01
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