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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用于本地模型网络识别的流数据增量模糊 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。模型的结构以 Takagi-Sugeno 形式作为局部模型网络给出，输入输出空间的划分基于这些局部模型被定义为集群原型的度量。这意味着集群和局部模型共享相同的参数；因此，与具有相当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不是一个特殊问题。该算法以增量方式添加局部模型并递归地调整局部模型参数。所提出的算法在三个示例上进行了测试，以展示主要特征。第一个示例是具有相交簇的简单模拟示例；第二个是一个非常著名的基准，用于处理 Mackey-Glass 时间序列；第三个示例显示了激光测距仪数据的分类。这些例子显示了所提出的方法在某些应用中的巨大潜力。