Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8069307 | Annals of Nuclear Energy | 2014 | 7 Pages |
Abstract
In this paper, an artificial neural network controller is presented using the Multifeedback-Layer Neural Network (MFLNN), which is a recently proposed recurrent neural network, for neutronic power level control of a nuclear research reactor. Off-line learning of the MFLNN is accomplished by the Particle Swarm Optimization (PSO) algorithm. The MFLNN-PSO controller design is based on a nonlinear model of the TRIGA Mark-II research reactor. The learning and the test processes are implemented by means of a computer program at different power levels. The simulation results obtained reveal that the MFLNN-PSO controller has a remarkable performance on the neutronic power level control of the reactor for tracking the step reference power trajectories.
Keywords
Related Topics
Physical Sciences and Engineering
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Energy Engineering and Power Technology
Authors
Ramazan Coban,