Adaptive neuro-fuzzy inference systems (ANFISs) deriving from big data bring us the perspective in many fields. However, online performing both processing noisy and massive databases (NMDs) and training ANFISs is a challenge. Inspired by this aim, we propose a strategy with two phases, offline and online. The offline discovers an optimal data screening threshold (ODST) which is interpreted as an index to measure the uncertainty of the data in a data cluster. A new algorithm named A-ODST is proposed to estimate the ODST. Using the kernel fuzzy C-means clustering technique, a new filter named FbMU for blurring the ODST-based measured data-uncertainty is presented. An improved algorithm named NMD-ANFIS is presented to build the ANFIS from the NMD filtered by the FbMU. Based on the three main contributions of this paper to be the A-ODST, FbMU, and NMD-ANFIS, processing NMD and training ANFIS can be performed synchronously in the online phase. The combination of the solution to optimize the cluster data space via the NMD-ANFIS to simplify ANFIS’s structure and the filtering strategy of the FbMU for removing all the data points belonging to the data clusters with the highest uncertainty allows both filtering noise and reducing the size of the database to improve the calculating cost. Surveys from two experimental systems were carried out to verify these aspects. The compared results showed that the predicting error and the calculating time of the ANFISs built by the proposed method were better than that from the other surveyed methods.

中文翻译：

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通过数据不确定性量化机制处理在线大规模测量数据库以合成ANFIS

源自大数据的自适应神经模糊推理系统（ANFIS）为我们带来了许多领域的观点。然而，在线执行处理噪声数据库和海量数据库（NMD）以及培训ANFIS都是一个挑战。受此目标的启发，我们提出了一个分为两个阶段的离线和在线策略。脱机发现最佳数据筛选阈值（ODST），该阈值被解释为衡量数据集群中数据不确定性的指标。提出了一种新的算法A-ODST来估计ODST。利用核模糊C均值聚类技术，提出了一种新的名为FbMU的滤波器，用于模糊基于ODST的测量数据的不确定性。提出了一种名为NMD-ANFIS的改进算法，以通过FbMU过滤后的NMD构建ANFIS。基于本文的三个主要贡献，即A-ODST，FbMU，和NMD-ANFIS，可以在在线阶段同步执行NMD处理和训练ANFIS。通过NMD-ANFIS优化群集数据空间以简化ANFIS的结构的解决方案与FbMU的过滤策略相结合，以去除属于不确定性最高的数据群集的所有数据点，从而既过滤噪声又减小了尺寸数据库的计算成本降低。从两个实验系统进行了调查，以验证这些方面。比较结果表明，该方法建立的ANFIS的预测误差和计算时间均优于其他方法。通过NMD-ANFIS优化群集数据空间以简化ANFIS的结构的解决方案与FbMU的过滤策略相结合，以去除属于不确定性最高的数据群集的所有数据点，从而既可以过滤噪声，又可以减小尺寸数据库的计算成本降低。从两个实验系统进行了调查，以验证这些方面。比较结果表明，该方法建立的ANFIS的预测误差和计算时间均优于其他方法。通过NMD-ANFIS优化群集数据空间以简化ANFIS的结构的解决方案与FbMU的过滤策略相结合，以去除属于不确定性最高的数据群集的所有数据点，从而既可以过滤噪声，又可以减小尺寸数据库的计算成本降低。从两个实验系统进行了调查，以验证这些方面。比较结果表明，该方法建立的ANFIS的预测误差和计算时间均优于其他方法。