Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2013, Vol. 24 ›› Issue (6): 060201 doi: 10.13538/j.1001-8042/nst.2013.06.005

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A Genetic-Algorithm-based Neural Network Approach for Radioactive Activity Prediction

WANG Lei1 TUO Xianguo1,2,* YAN Yucheng1 LIU Mingzhe1,* CHENG Yi1 LI Pingchuan1   

  1. 1State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
    2Southwest University of Science and Technology, Mianyang 621010, China
    3Southwest University of Science and Technology, Mianyang 621010, China
  • Contact: TUO Xianguo, LIU Mingzhe E-mail:tuoxg@swust.edu.cn; liumz@cdut.edu.cn
  • Supported by:

    Supported by National Natural Science Foundation of China (Nos.41025015, 41104118, 41274108, and 41274109), Special Program of Major Instruments of the Ministry of Science and Technology (No.2012YQ180118), Science and Technology Support Program of Sichuan Province (No.2013FZ0022) and the Creative Team Program of Chengdu University of Technology (No.KYTD201301).

WANG Lei, TUO Xianguo, YAN Yucheng, LIU Mingzhe. A Genetic-Algorithm-based Neural Network Approach for Radioactive Activity Prediction.Nuclear Science and Techniques, 2013, 24(6): 060201     doi: 10.13538/j.1001-8042/nst.2013.06.005

Abstract:

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.

Key words: Long range alpha detector, Genetic algorithms, Radioactivity, prediction