陈明高, 石仲堃. 遗传算法优化的RBF神经网络确定潜艇排水量和主尺度[J]. 中国舰船研究, 2006, 1(3): 38-40,46. DOI: 10.3969/j.issn.1673-3185.2006.03.009
引用本文: 陈明高, 石仲堃. 遗传算法优化的RBF神经网络确定潜艇排水量和主尺度[J]. 中国舰船研究, 2006, 1(3): 38-40,46. DOI: 10.3969/j.issn.1673-3185.2006.03.009
Chen Minggao, Shi Zhongkun. Determination of Submarine's Displacement & Principal Dimensions by Using GA Based Optimum RBF Neural Network[J]. Chinese Journal of Ship Research, 2006, 1(3): 38-40,46. DOI: 10.3969/j.issn.1673-3185.2006.03.009
Citation: Chen Minggao, Shi Zhongkun. Determination of Submarine's Displacement & Principal Dimensions by Using GA Based Optimum RBF Neural Network[J]. Chinese Journal of Ship Research, 2006, 1(3): 38-40,46. DOI: 10.3969/j.issn.1673-3185.2006.03.009

遗传算法优化的RBF神经网络确定潜艇排水量和主尺度

Determination of Submarine's Displacement & Principal Dimensions by Using GA Based Optimum RBF Neural Network

  • 摘要: 潜艇设计中,排水量和主尺度的确定是潜艇概念设计中最重要的一环。提出应用遗传算法训练径向基函数神经网络的方法,计算常规潜艇的排水量和主尺度。实验结果表明此方法训练效率高、精度高具有全局搜索能力。

     

    Abstract: A method is presented to train radial basis function(RBF) neural network with genetic algorithms. This method is used to calcalate the displacement and main dimensions of submarines. The results show the advantages of this fast training process, good comprehensive searching ability and high precission.

     

/

返回文章
返回