Open AccessMultimodel System Identification Based on New Fuzzy Partitioning Similarity Measure
Author(s) -
Abdelhadi Radouane,
F. Giri,
Amine NaïtAli,
F.Z. Chaoui
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i9290.0710921
Subject(s) - euclidean distance , benchmark (surveying) , measure (data warehouse) , space partitioning , cluster analysis , similarity measure , metric (unit) , nonlinear system , computer science , similarity (geometry) , feature vector , representation (politics) , identification (biology) , fuzzy logic , basis (linear algebra) , mathematics , euclidean space , data mining , artificial intelligence , algorithm , operations management , law , image (mathematics) , biology , geometry , geodesy , quantum mechanics , political science , physics , botany , politics , pure mathematics , economics , geography
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.