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

作　　者：WANG Lei TUO Xianguo YAN Yucheng LIU Mingzhe CHENG Yi LI Pingchuan 

机构地区：[1]State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology [2]Southwest University of Science and Technology

出　　处：《Nuclear Science and Techniques》2013年第6期12-16,共5页核技术（英文）

基　　金：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);the Creative Team Program of Chengdu University of Technology(No.KYTD201301)

摘　　要：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.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.

关 键 词：神经网络方法 遗传算法 模型预测 放射性 人工神经网络 活性 逼近能力 全局优化 

分 类 号：TL81[核科学技术—核技术及应用]