自然科学版 英文版
自然科学版 英文版
自然科学版 英文版

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中南大学学报(英文版)

Journal of Central South University

Vol. 11    No. 2    June 2004

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Neural network based on adaptive chaotic gradient descending
optimization algorithm and its application in matte converting process
HU Zhi-kun(胡志坤)1,2, PENG Xiao-qi(彭小奇)1, GUI Wei-hua(桂卫华)2

1.School of Physics Science and Technology, Central South University, Changsha 410083, China;
2.School of Information Science and Engineering, Central South University, Changsha 410083, China

Abstract:An adaptive chaotic gradient descending optimization algorithm for single objective optimization was present-ed. A local minimum judged by two rules was obtained by an improved mutative-step gradient descending method. A new optimal minimum was obtained to replace the local minimum by mutative-scale chaotic search algorithm whose scales are magnified gradually from a small scale in order to escape local minima. The global optimal value was attained by repeatedly iterating. At last, a BP (back-propagation) neural network model for forecasting slag output in matte converting was es-tablished. The algorithm was used to train the weights of the BP neural network model. The simulation results with a training data set of 400 samples show that the training process can be finished within 300 steps to obtain the global optimal value, and escape local minima effectively. An optimization system for operation parameters, which includes the forecast-ing model, is achieved, in which the output of converter increases by 6.0%, and the amount of the treated cool materials rises by 7.8% in the matte converting process.

 

Key words: matte converting; chaotic search; gradient descending; neural network

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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