Detecting unexperienced events via analysis of error propagation in a neuro-fuzzy signal processing system

R. Kozma, S. Sato, M. Sakuma, M. Kitamura

研究成果: Paper査読

1 被引用数 (Scopus)

抄録

The performance of system identification methods based on multi-layer artificial neural network (ANN) models is analyzed. The state of the system is monitored by multiple ANNs which act as individual neuro agents. An algorithm based on fuzzy logics is applied to combine information from ANNs. In this paper, we elaborate a method which can make a distinction between the occurrence of unexperienced events and any inconsistency in the judgments of agents caused by statistical uncertainties in the actual data. The results are illustrated by analyzing signals of numerical experiments and also actual measurements in a nuclear reactor.

本文言語English
ページ241-246
ページ数6
出版ステータスPublished - 1994
イベントProceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94) - St. Louis, MO, USA
継続期間: 1994 11 131994 11 16

Conference

ConferenceProceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94)
CitySt. Louis, MO, USA
Period94/11/1394/11/16

ASJC Scopus subject areas

  • 工学(全般)

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