Comparison of two on-line hybrid system identification methods

T. Alizadeh, A. Alizadeh, S. Sepasi, S. Barzegary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper we compare two recently proposed algorithms for online identification of hybrid systems. We consider the adaptive growing and pruning Radial Basis Function (RBF) neural network based and the potential fuzzy clustering based procedures. Specific behaviors of the procedures are pointed out, using a well known two dimensional example.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010
Pages1020-1023
Number of pages4
Publication statusPublished - Dec 1 2010
EventInternational MultiConference of Engineers and Computer Scientists 2010, IMECS 2010 - Kowloon, Hong Kong
Duration: Mar 17 2010Mar 19 2010

Publication series

NameProceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010

Other

OtherInternational MultiConference of Engineers and Computer Scientists 2010, IMECS 2010
CountryHong Kong
CityKowloon
Period3/17/103/19/10

Keywords

  • Hybrid systems - identification - GAPRBF neural network - potential Fuzzy Clustering

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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  • Cite this

    Alizadeh, T., Alizadeh, A., Sepasi, S., & Barzegary, S. (2010). Comparison of two on-line hybrid system identification methods. In Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010 (pp. 1020-1023). (Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010).