In this paper,a genetic-algorithm-based artificial neural network(GAANN)model radioactivity prediction is proposed,which is verified by measuring results from Long Range Alpha Detector(LRAD).GAANN can integrate capabilities of approximation of Artificial Neural Networks(ANN)and of global optimization of Genetic Algorithms(GA)so that the hybrid model can enhance capability of generalization and prediction accuracy,theoretically.With this model,both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation.The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation(BP)neural network,showing the feasibility and validity of the proposed approach.
WANG LeiTUO XianguoYAN YuchengLIU MingzheCHENG YiLI Pingchuan
Long-range alpha detectors (LRADs) are attracting much attention in the decommissioning of nuclear facilities because of some problems in obtaining source positions on an interior surface during pipe decommissioning. By utilizing the characteristic that LRAD detects alphas by collecting air-driving ions, this article applies a method to localize the radioactive source by ions' fluid property. By obtaining the ion travel time and the airspeed distribution in the pipe, the source position can be determined. Thus this method overcomes the ion's lack of periodic characteristics. Experimental results indicate that this method can approximately localize the source inside the pipe. The calculation results are in good agreement with the experimental results.
