Title :
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
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;
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
DOI :
10.1109/ICPR.1994.576993
