L∞ identification and model reduction using a learning genetic algorithm

Kay Chen Tan, Yun Li

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This paper develops a Boltzmann learning enhanced genetic algorithm for L norm based system identification and model reduction for robust control applications. Using this technique, both a globally optimised nominal model and an error bounding function for additive and multiplicative uncertainties can be obtained. It can also offer a tighter L error bound and is applicable to both continuous and discrete-time systems.

Original languageEnglish
Pages (from-to)1125-1130
Number of pages6
JournalIEE Conference Publication
Issue number427 /2
Publication statusPublished - 1 Dec 1996
Externally publishedYes


  • genetic algorithms
  • system identification
  • model reduction
  • robust control applications


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