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

Author

Nissan, Ephraim ; Siegelmann, Hava ; Galperin, Alex

Author_Institution

Dept. of Math., Bar-Ilan Univ., Ramat-Gan, Israel

Volume

2

fYear

1994

fDate

9-13 Oct 1994

Firstpage

494

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 NEURALIZER, 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

Keywords

nuclear engineering computing; FUELCON; NEL; NEURALIZER; engineering expert systems; fuel assembly configurations; machine learning; neural network architecture; nuclear engineering; nuclear reactor core reloading; ruleset revision; symbolic architecture; symbolic-to-neural conversion language; symbolic-to-neural conversion schema; Assembly; Design engineering; Expert systems; Fuels; Intelligent networks; Machine learning; Neural networks; Nuclear power generation; Quality management; Security;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on

Conference_Location

Jerusalem

Print_ISBN

0-8186-6270-0

Type

conf

DOI

10.1109/ICPR.1994.576993

Filename

576993