Masked Multiple State Space Model Identification Using FRD and Evolutionary Optimization

EFE M. Ö., KÜRKÇÜ B., KASNAKOĞLU C., Mohamed Z., Liu Z.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol.20, no.7, pp.9861-9869, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 7
  • Publication Date: 2024
  • Doi Number: 10.1109/tii.2024.3388605
  • Journal Name: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.9861-9869
  • Hacettepe University Affiliated: Yes

Abstract

Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector containing (A, B, C, D) quadruple's numerical content and a FRD-associated mask vector set that approximates the spectral information available in each FRD set? This article proposes a genetic algorithm based optimization approach to determine the real parameter vector (A, B, C, D) and the binary mask vector through a sequential optimization scheme. We study state space models for matching FRD from multiple systems. Results show that the proposed optimization approach solves the problem and compresses multiple dynamical models into a single masked one.