Function approximation using LVQ and fuzzy sets

Shon Min-Kyu, Junichi Murata, Kotaro Hirasawa

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


Neural networks with local activation functions, for example RBFNs (Radial Basis Function Networks), have a merit of excellent generalization abilities. When this type of network is used in function approximation, it is very important to determine the proper division of the input space into local regions to each of which a local activation function is assigned. In RBFNs, this is equivalent to determination of the locations and the numbers of its RBFs, which is generally done based on the distribution of input data. But, in function approximation, the output information (the value of the function to be approximated) must be considered in determination of the local regions. A new method is proposed that uses LVQ network to approximate the functions based on the output information. It divides the input space into regions with a prototype vector at the center of each region. The ordinary LVQ, however, outputs discrete values only, and therefore can not approximate continuous functions. In this paper, fuzzy sets are employed in both of learning and output calculation. Finally, the proposed method uses the back-propagation algorithm for fine adjustment. An example is provided to show the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)1442-1447
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2001
Event2001 IEEE International Conference on Systems, Man and Cybernetics - Tucson, AZ, United States
Duration: Oct 7 2001Oct 10 2001

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture


Dive into the research topics of 'Function approximation using LVQ and fuzzy sets'. Together they form a unique fingerprint.

Cite this