Clustering regression models fitted on the dataset is one of the most ubiquitous issues in different fields of sciences. In this research, fuzzy clustering method is used to cluster regression models with fractional Brownian motion errors that can be fitted on a dataset. Thereafter the performance of proposed approach is studied in simulated and real situations. The results verify that the introduced technique has excellent power to cluster the models. It indicates that our proposed method obtain many advantages. The performance of proposed technique is allowable. In addition, the algorithm is not so complicated. Furthermore, this method can be employed to compare both linear and nonlinear models.

数据集上拟合的聚类回归模型是不同科学领域中最普遍存在的问题之一。在这项研究中，使用模糊聚类方法对具有分数布朗运动误差的回归模型进行聚类，这些误差可以拟合到数据集上。此后，在模拟和真实情况下研究所提出方法的性能。结果证明，引入的技术具有出色的模型聚类能力。这表明我们提出的方法具有很多优点。所提出技术的性能是允许的。另外，该算法不是那么复杂。此外，该方法可用于比较线性模型和非线性模型。