Fisher information matrix based time-series segmentation of process data

László Dobos, János Abonyi

Research output: Contribution to journalArticle

3 Citations (Scopus)


Advanced chemical process engineering tools, like model predictive control or soft sensor solutions require proper process models. Parameter identification of these models needs input-output data with high information content. When model based optimal experimental design techniqes cannot be applied, the extraction of informative segements from historical data can also support system identification. We developed a goal-oriented Fisher information based time-series segmentation algorithm, aimed at selecting informative segments from historical process data. The utilized standard bottom-up algorithm is widely used in off-line analysis of process data. Different segments can support the identification of parameter sets. Hence, instead of using either D- or E-optimality as the criterion for comparing the information content of two input sequences (neigbouring segments), we propose the use of Krzanowski's similarity coefficient between the eigenvectors of the Fisher information matrices obtained from the sequences. The efficiency of the proposed methodology is demonstrated by two application examples. The algorithm is capable to extract segments with parameter-set specific information content from historical process data.

Original languageEnglish
Pages (from-to)99-108
Number of pages10
JournalChemical Engineering Science
Publication statusPublished - Sep 20 2013


  • Fisher information matrix
  • Optimal experimental design (OED)
  • Parameter identification
  • Process model
  • Time-series segmentation

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'Fisher information matrix based time-series segmentation of process data'. Together they form a unique fingerprint.

  • Cite this