Deling Zheng, Ruixin Liang, Ying Zhou, and Ying Wang, A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal, J. Univ. Sci. Technol. Beijing, 10(2003), No. 2, pp. 68-71.
Cite this article as:
Deling Zheng, Ruixin Liang, Ying Zhou, and Ying Wang, A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal, J. Univ. Sci. Technol. Beijing, 10(2003), No. 2, pp. 68-71.
Deling Zheng, Ruixin Liang, Ying Zhou, and Ying Wang, A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal, J. Univ. Sci. Technol. Beijing, 10(2003), No. 2, pp. 68-71.
Citation:
Deling Zheng, Ruixin Liang, Ying Zhou, and Ying Wang, A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal, J. Univ. Sci. Technol. Beijing, 10(2003), No. 2, pp. 68-71.
A genetic algorithm based on the nested intervals chaos search (NICGA) has been given. Because the nested intervals chaos search is introduced into the NICGA to initialize the population and to lead the evolution of the population, the NICGA has the advantages of decreasing the population size, enhancing the local search ability, and improving the computational efficiency and optimization precision. In a multi-layer feed forward neural network model for predicting the silicon content in hot metal, the NICGA was used to optimize the connection weights and threshold values of the neural network to improve the prediction precision. The application results show that the precision of predicting the silicon content has been increased.
A genetic algorithm based on the nested intervals chaos search (NICGA) has been given. Because the nested intervals chaos search is introduced into the NICGA to initialize the population and to lead the evolution of the population, the NICGA has the advantages of decreasing the population size, enhancing the local search ability, and improving the computational efficiency and optimization precision. In a multi-layer feed forward neural network model for predicting the silicon content in hot metal, the NICGA was used to optimize the connection weights and threshold values of the neural network to improve the prediction precision. The application results show that the precision of predicting the silicon content has been increased.
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