Fuzzy neurons and fuzzy multilinear mappings

K. S. Adbukhalikov, Chul Kim, H. S. Cho

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


There are several mathematical models of fuzzy neurons. Usually, input values of them are fuzzy numbers (that is, fuzzy sets in one-dimensional space) with triangular membership functions. The aggregating operations may be one of the T-norms or T-conorms. There are currently few, if any, learning methods proposed in the literature. We propose to consider fuzzy linear spaces as fuzzy inputs of fuzzy neurons and offer mathematical theory to work with this notions. In particular, we study fuzzy multilinear maps of fuzzy linear spaces. If the neuron model were to carry out only linear operations, the method would lose its mathematical attractiveness, but this is overcome by considering multidimensional linear spaces.

Original languageEnglish
Title of host publication1997 IEEE International Conference on Neural Networks, ICNN 1997
Number of pages5
Publication statusPublished - 1997
Externally publishedYes
Event1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States
Duration: Jun 9 1997Jun 12 1997

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576


Conference1997 IEEE International Conference on Neural Networks, ICNN 1997
Country/TerritoryUnited States
CityHouston, TX

ASJC Scopus subject areas

  • Software


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