Fuzzy learning model for membership function estimation and pattern classification

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

9 Citations (Scopus)


In this paper, a new fuzzy learning model called fuzzy learning vector quantization (FLVQ) which incorporates fuzzy clustering concept with Kohonen's learning vector quantization (LVQ) model is proposed. The new learning algorithm is derived from optimizing an appropriate fuzzy objective function which takes into accounts of two goals, namely, minimizing the differences between target and actual class membership outputs, and minimizing the distances between training patterns and neuron's parametric vectors. It retains the LVQ's reinforce-or-punish learning principle and more importantly introduces graded corrections. As compared with the LVQ algorithm, the proposed one is characterized by several distinctive features: i) avoiding neuron underutilization; ii) superior classification and generalization performances; and iii) insensitive to initial conditions. Since the outputs of the model have been formulated as fuzzy class membership functions, it can be readily used to estimate the membership functions of fuzzy systems. Furthermore, through the concept of 'clusters as rules', the trained network can be interpreted in the form of fuzzy IF-THEN rules. All these features of the proposed model are demonstrated through numerical examples.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Number of pages6
Publication statusPublished - 1 Dec 1994
Externally publishedYes
EventProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3) - Orlando, FL, United States
Duration: 26 Jun 199429 Jun 1994


ConferenceProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3)
Country/TerritoryUnited States
CityOrlando, FL

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality


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