Simplification of Neural Network Model for Predicting Local Power Distributions of BWR Fuel Bundle Using Learning Algorithm with Forgetting.

• TANABE Akira

  Toshiba Corp.

• YAMAMOTO Toru

  Toshiba Corp.

• SHINFUKU Kimihiro

  Toshiba Information System Corp.

• NAKAMAE Takuji

  Toshiba Information System Corp.

• NISHIDE Fusayo

  Toshiba Information System Corp.

Abstract

Previously a two-layered neural network model was developed to predict the relation between fissile enrichment of each fuel rod and local power distribution in a BWR fuel bundle. This model was obtained intuitively based on 33 patterns of training signals after an intensive survey of the models. Recently, a learning algorithm with forgetting was reported to simplify neural network models. It is an interesting subject what kind of model will be obtained if this algorithm is applied to the complex three-layered model which learns the same training signals. A three-layered model which is expanded to have direct connections between the 1st and the 3rd layer elements has been constructed and the learning method of normal back propagation was applied first to this model. The forgetting algorithm was then added to this learning process. The connections concerned with the 2nd layer elements disappeared and the 2nd layer has become unnecessary. It took a longer computing time by an order to learn the same training signals than the simple back propagation, but the two-layered model was obtained autonomously from the expanded three-layered model.

Journal

• Journal of Nuclear Science and Technology

  Journal of Nuclear Science and Technology 32 (2), 133-141, 1995

  Atomic Energy Society of Japan

Keywords

• BWR type reactor
• local power distribution
• fuel bundles
• neural networks
• artificial intelligence
• simplification
• forgetting
• learning algorithm
• training signal
• back propagation
• expanded three-layered model
• computer architecture

Details

• CRID: 1390001204095135872
• NII Article ID: 10002072060
• NII Book ID: AA00703720
• DOI: 10.3327/jnst.32.133
• COI: 1:CAS:528:DyaK2MXjvFChs7c%3D
• ISSN: 18811248
• ISSN: 00223131
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed