王建梅, 覃文忠. 基于L-M算法的BP神经网络分类器[J]. 武汉大学学报 ( 信息科学版), 2005, 30(10): 928-931.
引用本文: 王建梅, 覃文忠. 基于L-M算法的BP神经网络分类器[J]. 武汉大学学报 ( 信息科学版), 2005, 30(10): 928-931.
WANG Jianmei, QIN Wenzhong. BP Neural Network Classifier Based on Levenberg-Marquardt Algorithm[J]. Geomatics and Information Science of Wuhan University, 2005, 30(10): 928-931.
Citation: WANG Jianmei, QIN Wenzhong. BP Neural Network Classifier Based on Levenberg-Marquardt Algorithm[J]. Geomatics and Information Science of Wuhan University, 2005, 30(10): 928-931.

基于L-M算法的BP神经网络分类器

BP Neural Network Classifier Based on Levenberg-Marquardt Algorithm

  • 摘要: 以TM图像为例,讨论了基于Levenberg-Marquardt(L-M)算法的BP神经网络分类器及其在遥感图像分类中的应用。LM算法是梯度下降法与高斯牛顿法的结合,由于利用了近似的二阶导数信息,LM算法比梯度法快。就训练次数及准确度而言,LM算法明显优于变学习率法的BP算法。

     

    Abstract: BP Neural network classifier based on Levenberg-Marquardt (L-M) algorithm and its application to remote sensing image classification is discussed in this paper. L-M algorithm is a combination of gradient method and Gauss-Newton method. With the aid of the approximate second derivative, the L-M algorithm is more efficient than the gradient method. Concerned with the training process and accuracy, the L-M algorithm is superior to vary-learning\|rate BP method.

     

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