Neural network models for anytime use

Research output: Conference contribution

Abstract

Nowadays, the role of anytime and situational models and algorithms has become important because they offer a way to handle atypical situations and to overcome problems of resource, time, and data insuffiency in changing and time-critical systems and situations. Soft computing, in particular fuzzy and neural network based models are serious candidates for usage in such systems, however their high complexity, and in some cases unknown accuracy, can limit their applicability. In this paper, special neural network structures are introduced which (1) complexity can adaptively be chosen according to the temporal situation (resource, time, and data availability), (2) the accuracy is always known, and (3) monotonously decreases parallel with the increase of the complexity of the used model/algorithm.

Original languageEnglish
Title of host publicationINES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings
Pages95-100
Number of pages6
DOIs
Publication statusPublished - aug. 22 2011
Event15th International Conference on Intelligent Engineering Systems, INES 2011 - Poprad, Slovakia
Duration: jún. 23 2011jún. 25 2011

Publication series

NameINES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings

Other

Other15th International Conference on Intelligent Engineering Systems, INES 2011
CountrySlovakia
CityPoprad
Period6/23/116/25/11

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

Cite this

Várkonyi-Kóczy, A. R. (2011). Neural network models for anytime use. In INES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings (pp. 95-100). [5954727] (INES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings). https://doi.org/10.1109/INES.2011.5954727