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

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 prototy...

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Veröffentlicht in:IEEE transactions on fuzzy systems Jg. 28; H. 4; S. 758 - 767
Hauptverfasser: Blazic, Saso, Skrjanc, Igor
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2019.2916036