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APPLICATION OF NEURAL NETWORKS TO CONNECTIONIST EXPERT SYSTEM FOR IDENTIFICATION OF TRANSIENTS IN NUCLEAR POWER PLANTS

Se Woo Cheon
Dept. of Nuclear Eng., Korea Advanced Institute of Science and Technology, 373-1, Ku Song Dong, Yu Sung Gu, Taejon, Korea

Gi Sik Kang
Dept. of Nuclear Eng., Korea Advanced Institute of Science and Technology, 373-1, Ku Song Dong, Yu Sung Gu, Taejon, Korea

Soon-Heung Chang
Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Goo, TaeJon Korea

Myung Sub Roh
Research Center, Korea Electric Power Corporation, 117-15, Yong Chun Dong, Dong Gu, Taejon, Korea

Sinopsis

The expert systems that have neural networks for their knowledge bases are called connectionist expert systems. The back-propagation neural network model is applied to the connectionist expert system for the identification of transients in nuclear power plants. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are discussed. The general mapping capability of the neural networks enables to identify a transient easily. The case studies are performed with emphasis on the applicability of the neural networks to the classification problems. It is revealed that the neural network model can identify the transient properly, even when incomplete, untrained, or sensor-failed symptoms are given. It is also shown that multiple transients are easily identified.

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