In this paper, a new approach to evolving fuzzy model identification from streaming data is given. The structure of the model is given as a local model network in Takagi–Sugeno form, and the partitioning of the input–output space is based on metrics in which these local models are defined as prototypes of the clusters. This means that the clusters and the local models share the same parameters; therefore, the number of parameters of the evolving system is much lower in comparison to similar systems of comparable complexity, and the problems of parameter identifiability are not a particular issue. The algorithm adds the local models in an incremental fashion and recursively adapts the local model parameters. The proposed algorithm is tested on three examples to demonstrate the main features. The first example is a simple simulated example with intersecting clusters; the second is a very well-known benchmark that treats the Mackey–Glass time series; the third is an example that shows the classification of the data from a laser rangefinder. These examples show the great potential of the proposed approach in certain applications.

中文翻译：

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用于局部模型网络识别的流数据的增量模糊 C 回归聚类

在本文中，给出了一种从流数据演化模糊模型识别的新方法。该模型的结构以 Takagi-Sugeno 形式的局部模型网络给出，输入-输出空间的划分基于将这些局部模型定义为集群原型的度量标准。这意味着集群和本地模型共享相同的参数；因此，与具有可比复杂性的类似系统相比，演化系统的参数数量要少得多，并且参数可识别性问题并不是一个特别的问题。该算法以增量方式添加局部模型并递归地适应局部模型参数。所提出的算法在三个例子上进行了测试，以展示主要特征。第一个例子是一个简单的具有交叉簇的模拟例子；第二个是处理 Mackey-Glass 时间序列的非常著名的基准；第三个是显示来自激光测距仪的数据分类的示例。这些例子显示了所提出的方法在某些应用中的巨大潜力。