Multimodel System Identification Based on New Fuzzy Partitioning Similarity Measure
| Autor: | Abdelhadi Radouane, Fouad Giri, A. Naitali, Fatima-Zahra Chaoui |
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| Přispěvatelé: | Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science business.industry System identification Pattern recognition Fuzzy partitioning Similarity measure 100.1/ijitee.I92900710921 Mechanics of Materials MIMO Nonlinear Systems System Identification Local Linear Models Fuzzy Clustering Similarity Measure Angular Deviation Weighted Least-Squares (WLS) 2278-3075 Artificial intelligence Electrical and Electronic Engineering business Civil and Structural Engineering |
| Zdroj: | International Journal of Innovative Technology and Exploring Engineering. 10:19-30 |
| ISSN: | 2278-3075 |
| DOI: | 10.35940/ijitee.i9290.0710921 |
| Popis: | The problem of identifying unstructured nonlinear systems is generally addressed on the basis of multi-model representations involving several linear local models. In the present work, local models are combined to get a global representation using incremental fuzzy clustering. The main contribution is a novel vector similarity measure defined in the System Working Space (SWS) that combines the angular deviation and the usual Euclidean distance. Such a combination makes the new metric highly discriminating leading to a better partitioning of the operating space providing, thereby, a higher accuracy of the model. The developed partitioning method is first evaluated by performing linear local model (LLM) based identification of a academic benchmark multivariable nonlinear system. Then, the performances of the identification method are evaluated using experimental tropospheric ozone data. These evaluations illustrate the supremacy of the new method over the standard Euclidian-distance based partitioning approach. |
| Databáze: | OpenAIRE |
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