{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:09:54Z","timestamp":1777705794836,"version":"3.51.4"},"reference-count":45,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:p>In this paper, an interval type-2 evolving fuzzy Kalman filter is designed for processing of unobservable spectral components of uncertain experimental data. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi-Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer\/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral Analysis of the experimental data. The computational results for tracking the Mackey-Glass chaotic time series illustrate the efficiency of proposed methodology as compared to relevant approaches from literature, and the experimental results for tracking a 2DoF helicopter demonstrate its applicability.<\/jats:p>","DOI":"10.3233\/jifs-222919","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T12:15:06Z","timestamp":1679400906000},"page":"9379-9394","source":"Crossref","is-referenced-by-count":1,"title":["Type-2 evolving fuzzy Kalman filter design based on unobservable spectral components space for interval tracking of non-stationary experimental data"],"prefix":"10.1177","volume":"44","author":[{"given":"Daiana","family":"Gomes","sequence":"first","affiliation":[{"name":"Federal University of Maranh\u00e3o \u2013 UFMA, S\u00e3o Lu\u00eds, Maranh\u00e3o, 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