Abstract | ||
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This paper presents a new approach to neuro-fuzzy model identification based on a filtered recursive least squares method combined with an incrementally evolving Gaussian clustering method. The proposed identification algorithm generates the model on the fly and requires few user-defined parameters, which is one of the main advantages compared to other methods. The partitioning of the input-output data space depends on the chosen criteria and thresholds and depends only on the operating point of the model. As an example, the Wiener-Hammerstein type of a dynamic process was identified to show the potential of the proposed method in identifying nonlinear dynamic models. The Wiener-Hammerstein structure was used because a variety of processes can be modeled with this type of structure. Moreover, we tested the same identification concept on a real heat exchanger plant with strong nonlinear behavior. In addition, the limitations of the real sensors and actuators represent a serious challenge to the identification procedure. Both experiments, on a simulated Wiener-Hammerstein model and on a real plant, have shown that the proposed new neuro-fuzzy model identification with the new merging concept is very easy to implement, can perform all necessary calculations online, and can generate meaningful models. |
Year | DOI | Venue |
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2021 | 10.1109/ACCESS.2021.3130678 | IEEE ACCESS |
Keywords | DocType | Volume |
Data stream, evolving clustering, filtered recursive least squares identification, neuro-fuzzy models | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Goran Andonovski | 1 | 28 | 3.97 |
Edwin Lughofer | 2 | 1940 | 99.72 |
Igor Skrjanc | 3 | 354 | 52.47 |