An integrated symbolic and neural network architecture for machine learning in the domain of nuclear engineering

Ephraim Nissan, Hava Siegelmann, Alex Galperin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

On top of FUELCON and NEL, two extant, successful projects in, respectively, expert systems for engineering, and neural networks, we have defined and designed a new phase, meant to greatly increase the significance, for AI, of the combined project with respect to the already recognized merits of the two seed-projects. The NEL symbolic-to-neural conversion schema and language is resorted to in NEU-RALIZER, a component meant to automatically revise a ruleset, iteration after iteration, within the operation cycle of FUELCON, a generator of families of configurations of fuel assemblies for reloading the core of nuclear reactors.

Original languageEnglish
Title of host publicationProceedings of the 12th IAPR International Conference on Pattern Recognition - Conference B
Subtitle of host publicationPattern Recognition and Neural Networks, ICPR 1994
PublisherInstitute of Electrical and Electronics Engineers
Pages494-496
Number of pages3
ISBN (Electronic)0818662700
StatePublished - 1 Jan 1994
Event12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994 - Jerusalem, Israel
Duration: 9 Oct 199413 Oct 1994

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Conference

Conference12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994
Country/TerritoryIsrael
CityJerusalem
Period9/10/9413/10/94

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'An integrated symbolic and neural network architecture for machine learning in the domain of nuclear engineering'. Together they form a unique fingerprint.

Cite this