Improving classification performance of sonar targets by applying general regression neural network with PCA
Improving classification performance of sonar targets by applying general regression neural network with PCA
复制标题
DOI：
10.1016/j.eswa.2007.07.021
复制
发表时间：
2008-07-01
影响因子：
8.5
通讯作者：
Yildirim, Tuelay
中科院分区：
文献类型：
--
作者：
Erkmen, Burcu;Yildirim, Tuelay
利用主动声纳对浅海沃茨进行远程探测是维护重要港口和航道安全的重要课题。由人类专家进行的水雷和海底岩石的分辨声纳回波通常是困难的，工作量非常大。神经网络分类器由于其自适应性和并行处理能力，在复杂声纳信号分类中得到了广泛的应用。本文利用广义回归神经网络（GRNN）在学习速度快、样本数大时能收敛到最优回归曲面的优点，将其应用于水下目标分类问题。主成分分析（PCA）已被建立作为一种特征提取方法，以提高分类性能。接受者操作特征（ROC）分析已应用于神经分类器，以评估诊断程序的灵敏度和特异性。(c)2007爱思唯尔有限公司版权所有。
The remote detection of undersea mines in shallow waters using active sonar is a crucial subject required to maintain the security of important harbors and cost line areas. The discrimination sonar returns from mines and returns from rocks on the sea floor by human experts is usually difficult and very heavy workload. Neural network classifiers have been widely used in classification of complex sonar signals due to its adaptive and parallel processing ability. In this paper, due to the advantages on fast learning and convergence to the optimal regression surface as the number of samples becomes very large, general regression neural network (GRNN) has been used to solve the problem of classification underwater targets. Principal component analysis (PCA) has been established as a feature extraction method to improve classification performance. Receiver operating characteristic (ROC) analysis has been applied to the neural classifier to evaluate the sensitivity and specificity of diagnostic procedures. (c) 2007 Elsevier Ltd. All rights reserved.