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Evolving Fuzzy Kalman Filter: A Black-Box Modeling Approach Applied to Rocket Trajectory Forecasting

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Book cover Fuzzy Information Processing (NAFIPS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 831))

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Abstract

A methodology to system identification based on Evolving Fuzzy Kalman Filter is proposed in this paper. The mathematical formulation using an evolving Takagi-Sugeno (TS) structure is presented: the offline Gustafson Kessel (GK) Algorithm is used to a initial data set; after that, an evolving GK algorithm estimate the antecedent parameters. A fuzzy version OKID (Observer/Kalman Filter Identification) algorithm is formulated to obtain the matrices A, B, C, D, and K (state matrix, input influence matrix, output influence matrix, direct transmission matrix, and Kalman gain matrix, respectively), recursively, composing the consequent parameters. Experimental results from black-box modeling applied to rocket trajectory forecasting show the efficiency and applicability of the proposed methodology.

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Acknowledgment

This work was supported by FAPEMA, IFMA and encouraged by Ph.D. Program in Electrical Engineering of Federal University of Maranhão (PPGEE/UFMA).

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Authors and Affiliations

  1. Department of Electroelectronics, Laboratory of Computational Intelligence Applied to Techonology, Federal Institute of Education, Science and Technology, São Luís, MA, Brazil

    Danúbia Soares Pires & Ginalber Luiz de Oliveira Serra

Authors
  1. Danúbia Soares Pires
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  2. Ginalber Luiz de Oliveira Serra
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Corresponding author

Correspondence to Danúbia Soares Pires .

Editor information

Editors and Affiliations

  1. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Guilherme A. Barreto

  2. Department of Statistics & Applied Mathematics, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Ricardo Coelho

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Pires, D.S., de Oliveira Serra, G.L. (2018). Evolving Fuzzy Kalman Filter: A Black-Box Modeling Approach Applied to Rocket Trajectory Forecasting. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_29

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  • DOI: https://doi.org/10.1007/978-3-319-95312-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95311-3

  • Online ISBN: 978-3-319-95312-0

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