Acta Metallurgica Sinica (English Letters) ›› 2011, Vol. 24 ›› Issue (1): 34-42.DOI: 10.11890/1006-7191-111-34

• 研究论文 • 上一篇    下一篇

基于增量改进BP神经网络的微波煅烧重铀酸铵的预测模型

李英伟1,刘秉国2,彭金辉1,李玮2   

  1. 1. 昆明理工大学
    2.
  • 收稿日期:2010-05-25 修回日期:2010-10-17 出版日期:2011-02-25 发布日期:2011-02-25
  • 通讯作者: 彭金辉

Prediction model of microwave calcining of ammonium diuranate using incremental improved back-propagation neural network

Yingwei LI1,2, Bingguo LIU1,2,Jinhui PENG1,2,Wei LI2, Daifu HUANG3, Libo ZHANG1,2   

  1. 1. Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology,Kunming 650093, China
    2. Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming 650093, China
    3. No.272 Nuclear Industry Factory, China National Nuclear Corporation, Hengyang 421002, China
  • Received:2010-05-25 Revised:2010-10-17 Online:2011-02-25 Published:2011-02-25
  • Contact: Jinhui PENG

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摘要： 本文提出一种采用Levenberg-Marquardt优化算法的增量改进BP神经网络预测模型，能够解决微波煅烧重铀酸铵过程中出现的种种问题，例如测试周期长，测试量大，难以优化过程参数，试验数据无法一次性提供，它是以增量方式提供和系统内存的限制使得当样本规模较大时训练不可行。基于增量改进BP神经网络能够避免网络陷入局部最小的问题，能够寻找全局最优解，加快收敛速度等，建立了非线性系统的预测模型，它能够有效地预测微波煅烧重铀酸铵试验过程结果。

Abstract: The incremental improved Back-Propagation (BP) neural network prediction model using the Levenberg-Marquardt algorithm based on optimizing theory is put forward, which can solve the problems existing in the process of calcinations for ammonium diuranate (ADU) by microwave heating, such as long testing cycle, high testing quantity, difficulty of optimization for process parameters. Many training data probably were offered by the way of increment batch and the limitation of the system memory could make the training data infeasible when the sample scale was large. The prediction model of the nonlinear system is built, which can effectively predict the experiment of microwave calcining of ADU, and the incremental improved BP neural network is very useful in overcoming the local minimum problem, finding the global optimal solution and accelerating the convergence speed.

Key words: Microwave calcinations, ADU, Increment, BP neural network, Prediction