Optimal input design for reduction of parameter correlations

Ke Wang, Hong Yue, Hui Yu

Research output: Contribution to conferenceProceedingpeer-review

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An new scalarisation criterion is proposed for optimal experiment design (OED) of input intensity so as to obtain the most informative experimental data for parameter estimation with reduced parameter correlations. This criterion is a linear combination of logarithm function of the A-optimality and the modified E (ME)-optimality. It can be used to improve the estimation quality from the A-optimal design, and to reduce parameter correlations from the MEoptimal design. The proposed algorithm has been examined through simulation study of an enzyme reaction system model. The results are compared with A-optimal design, MEoptimal design, and other designs with a focus on reducing parameter correlations such as the C- and the CE- designs.
Original languageEnglish
Number of pages6
Publication statusPublished - 6 Sept 2018
EventThe 24th International Conference on Automation and Computing (ICAC'18) - Newcastle University, Newcastle, United Kingdom
Duration: 6 Sept 20187 Sept 2018


ConferenceThe 24th International Conference on Automation and Computing (ICAC'18)
Abbreviated titleICAC'18
Country/TerritoryUnited Kingdom
Internet address


  • parameter estimation
  • optimal experimental design (OED)
  • optimal input design
  • reduction of parameter correlation


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