摘要
本文提出了一种模拟退火免疫算法,该算法借鉴生物免疫系统的免疫识别、多样性及学习功能,利用基于模拟退火的浓度调节抗体多样性保持机理克服遗传算法易早熟收敛的缺点。将此方法用于优化铁水含硅量神经网络预报模型中的连接权值和阈值,可避免陷入局部极小,从而得到最佳神经网络,提高铁水含硅量预报精度。仿真结果证明了方法的有效性。
A simulated-annealing-based immune algorithm (SAIA) is presented in the paper. By imitating the biological immune system' s characteristics of immune recognition and learning to respond to invading antigens, the algorithm can restrain the degenerate phenomenon by using the immune mechanism based on the simulated annealing to maintain the individual diversity. In a neural network prediction model of the silicon content in hot metal, the SAIA is used to optimize the connection weights of a multi-layer feed forward neural network to improve the prediction precision. The testing and simulating results are satisfactory.
出处
《信息与控制》
CSCD
北大核心
2003年第4期335-338,共4页
Information and Control
基金
国家自然科学基金(69772014)
关键词
高炉炼铁
铁水
含硅量
预报
模拟退火免疫算法
simulated annealing
optimization
immune algorithm
neural network
Si prediction
