Filters
Results 1 - 1 of 1
Results 1 - 1 of 1.
Search took: 0.027 seconds
Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.
Proceedings of specialists' meeting on application of artificial intelligence and robotics to nuclear plants1994
Proceedings of specialists' meeting on application of artificial intelligence and robotics to nuclear plants1994
AbstractAbstract
[en] In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)
Primary Subject
Source
Japan Atomic Energy Research Inst., Tokyo (Japan); 431 p; 1994; p. 313-322; AIR'94: specialists' meeting on application of artificial intelligence and robotics to nuclear plants; Tokai, Ibaraki (Japan); 30 May - 1 Jun 1994
Record Type
Miscellaneous
Literature Type
Conference
Report Number
Country of publication
Reference NumberReference Number
Related RecordRelated Record
INIS VolumeINIS Volume
INIS IssueINIS Issue