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Optimal linear neuron learning and Kalman Filter based backpropagation neural network for DGPS/INS integration

机译：用于DGPS / INS集成的最优线性神经元学习和基于卡尔曼滤波的反向传播神经网络

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This paper introduces an optimal least mean square (LMS) rule for a linear neuron DGPS/INS integration method. The optimal LMS rule is based on an online calculation of the learning rate based on the minimum variance criteria. Then, using this rule, the n

机译：本文介绍了线性神经元DGPS / INS集成方法的最佳最小均方（LMS）规则。最佳LMS规则基于基于最小方差标准的学习率的在线计算。然后，使用此规则，

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《Quantum Nano Micro Technologies, 2007 1st》|2008年|P.1175-1189|共15页
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Ibrahim; F.A.;
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中图分类工业技术;
关键词
Global Positioning System; Kalman filters; adaptive estimation; backpropagation; least mean squares methods; neural nets; telecommunication computing; DGPS; INS error sources estimation; INS integration; Kalman filter; LMS; backpropagation neural network training me;

机译：全球定位系统;卡尔曼滤波器;自适应估计;反向传播;最小均方方法;神经网络;电信计算; DGPS; INS误差源估计; INS集成;卡尔曼滤波器; LMS;反向传播神经网络训练我;

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4. Optimal linear neuron learning and Kalman Filter based backpropagation neural network for DGPS/INS integration [C] . Ibrahim, F.A. Quantum Nano Micro Technologies, 2007 1st . 2008

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Optimal linear neuron learning and Kalman Filter based backpropagation neural network for DGPS/INS integration

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