Incremental Fuzzy C-Regression Clustering From Streaming Data for Local-Model-Network Identification

• Published in: IEEE transactions on fuzzy systems 2020-04, Vol.28 (4), p.758-767
• Main Authors: Blazic, Saso, Skrjanc, Igor
• Format: Article
• Language: English
• Subjects: Adaptation models; Algorithms; Clustering; Clustering algorithms; Computer simulation; Data models; Evolution; Evolving fuzzy model identification; fuzzy C-regression clustering; incremental clustering; Laser range finders; Linear programming; Mathematical models; Parameter identification; Partitioning algorithms; Prototypes; stream data; Takagi-Sugeno model
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2019.2916036

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.