Using recurrent neural networks for estimation of minor actinides’ transmutation in a high power density fusion reactor

íbeyli, Mustafa; íbeylï, Elif Derya

doi:10.1016/j.eswa.2009.08.005

Cited by 5 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of artificial intelligence, neural network is the most common used method, the nuclear power plant (NPP) is partitioned to small separate fault diagnosis systems to decrease the quantity of input/output data and make the best processing data that does not confuse neural network with all data of the NPP in same program, and then the outputs of all programs will be collected in a global fault diagnosis program [3,4,5]. With the weak feature such as Behavior due to unknown input pattern signals, and to overcome the weak feature a fuzzy logic stage prior to the neural network stage is added.…”

Section: Introductionmentioning

confidence: 99%

Safety Improvement of Nuclear Power Reactor Using Soft Computing Techniques

Muzzammil¹

2013

IOSR-JECE

View full text Add to dashboard Cite

Nowadays, Nuclear Reactors (NR) are large in scale and complex, they are expected to be operated with high levels of reliability and safety. Hence to increase plant safety, to achieve, maintain system stability and assure satisfactory in order to meet the increasing demands for automated system and to detect and diagnose system failures and malfunctions. When a plant malfunction occurs, a great data influx is occurred. This paper proposes a support system based on neuro fuzzy approach conjunction with Genetic Algorithm that assist alarming and diagnosis system. Throughout this framework Neurofuzzy fault diagnosis system is employed to diagnosis the fault of nuclear reactors. Hence to overcome weak points of both neuro learning and linguistic based approaches by which the integrated system will inherit the strength of both approaches and to optimize the Neurofuzzy outcomes using Genetic Algorithm resulting to show the efficiency is obtained by GA is greater and the inaccurate information of the alarming system also compared with Neurofuzzy diagnosis system.

show abstract

Section: Introductionmentioning

confidence: 99%