神经网络训练中扩展卡尔曼滤波与Levenberg-Marquardt方法的比较

IX Latin American Robotics Symposium and IEEE Colombian Conference on Automatic Control, 2011 IEEE Pub Date : 2011-11-28 DOI:10.1109/LARC.2011.6086835

P. Deossa, J. Patino, J. Espinosa, F. Valencia

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引用次数: 6

摘要

本文比较了Levenverg-Marquardt和扩展卡尔曼滤波方法在神经网络训练中的性能。作为测试平台，利用实验测量得到的RSSI数据，利用神经网络解决室内定位问题。使用这两种方法对网络进行训练，并采用均方误差(MSE)作为性能指标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A comparison of Extended Kalman Filter and Levenberg-Marquardt methods for neural network training

This paper presents a performance comparison of both the Levenverg-Marquardt and Extended Kalman Filter methods for neural network training. As a testbed, an indoor localization problem was solved by the neural network from the RSSI data obtained through a experimental measurement. Both methods were used to train the network, and the MSE (mean squared error) was employed as the performance metric.

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IX Latin American Robotics Symposium and IEEE Colombian Conference on Automatic Control, 2011 IEEE

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