| 基于L-M贝叶斯正则化方法的BP神经网络在潜艇声纳部位自噪声预报中的应用 |
| |
| 引用本文： | 吴方良,石仲堃,杨向晖,王建.基于L-M贝叶斯正则化方法的BP神经网络在潜艇声纳部位自噪声预报中的应用[J].船舶力学,2007,11(1):136-142. |
| |
| 作者姓名： | 吴方良 石仲堃 杨向晖 王建 |
| |
| 作者单位： | 华中科技大学交通学院,武汉,430074;华中科技大学交通学院,武汉,430074;华中科技大学交通学院,武汉,430074;华中科技大学交通学院,武汉,430074 |
| |
| 摘 要： | 基于L-M贝叶斯正则化方法使BP神经网络在推广能力、收敛速度和逼近精度上能够获得很大的提高.文中将BP神经网络和L-M贝叶斯正则化算法相结合用于潜艇声纳部位自噪声预报.分析了影响声纳部位自噪声的各种参数.利用潜艇声纳实测数据进行网络训练,训练好的神经网络可以对潜艇声纳部位自噪声进行精确预报.
|
| 关 键 词： | 声纳自噪声 BP神经网络 贝叶斯正则化 |
| 文章编号： | 1007-7294(2007)01-0136-07 |
| 修稿时间： | 2006年4月6日 |
Submarine sonar self-noise forecast base on BP neural network and L-M Bayesian Regularization algorithm |
| |
| WU Fang-liang,SHI Zhong-kun,YANG Xiang-hui,WANG Jian.Submarine sonar self-noise forecast base on BP neural network and L-M Bayesian Regularization algorithm[J].Journal of Ship Mechanics,2007,11(1):136-142. |
| |
| Authors: | WU Fang-liang SHI Zhong-kun YANG Xiang-hui WANG Jian |
| |
| Abstract: | L-M(Levenberg-Marquart) Bayesian Regularization algorithm is combined with Back-Propagation neural network to conduce to better generalization and faster speed of convergence,and higher learning accuracy can be acquired when multiple parameters and large patterns are inputted.In this paper,Bayesian Regularization algorithm and BP neural network were combined to forecast submarine sonar self-noise.All kinds of parameters that have function to submarine sonar self-noise were analyzed.Actual data were utilized to train BP neural network that can be used to accurately forecast submarine sonar self-noise. |
| |
| Keywords: | sonar self-noise BP neural network Bayesian Regularization algorithms |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
