Hybrid evolutionary soft-computing approach for unknown system identification

Chunshien Li, Kuo Hsiang Cheng, Zen Shan Chang, Jiann Der Lee

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


A hybrid evolutionary neuro-fuzzy system (HENFS) is proposed in this paper, where the weighted Gaussian function (WGF) is used as the membership function for improved premise construction. With the WGF, different types of the membership functions (MFs) can be accommodated in the rule base of HENFS. A new hybrid algorithm of random optimization (RO) algorithm incorporated with the least square estimation (LSE) is presented. Based on the hybridization of RO-LSE, the proposed soft-computing approach overcomes the disadvantages of other widely used algorithms. The proposed HENFS is applied to chaos time series identification and industrial process modeling to verify its feasibility. Through the illustrations and comparisons the impressive performances for unknown system identification can be observed.

Original languageEnglish
Pages (from-to)1440-1449
Number of pages10
JournalIEICE Transactions on Information and Systems
Issue number4
StatePublished - Apr 2006


  • Hybrid learning
  • Neuro-fuzzy systems
  • Random optimization
  • System identification / modeling
  • Weighted Gaussian function


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