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??????????????BP?????緽??????????ι???????????????????????????????BP????????,???????ж?????????С????????????????С????????,???????????????????????????,?÷??????к??????????????????????     

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?????С???????????????????LS-SVM??????????μ???????????????????μ????????н???С???????????C-C??????????????????????????????????????????????????????????????????LS-SVM??????н?????????BP??????????????????????????????????????С???????LS-SVM????????????????????н??????Ч????     

Polar Sea Ice Identification and Classification Based on HY-2A/SCAT Data

In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification.     

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20????50????????????????????????????????????????????????????????????????????????????????????????1????????????????????????????????????ò???????????????????????BP???????????????????(GIS)??????????????????????????????????????????????????????????????y??????????????????????????????????á?     

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????????????????Kullback-Leiber???????????????Bayes?????????????????????Σ?????????????￡??????????????????????????????????????????????????3??????????Kullback-Leiber????????????????????????б???????÷???????????????????????????Ч????     

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????DORIS?????????????ENVISAT????????????NASA??????SRTM?????????DEM????????????????????????????????????α??????????????????????????????????????????????????ε??????????????????????????????????????????????????????25.2 cm??     

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????GLONASS???????????????????????????GLONASS??????????????????????????GLONASS??????????????λ????????GLONASS???????????????IGS??????????е?????λ???????????????????????GLONASS????????????????????????????λ??????     

����Tongji-GRACE01ģ�͵��ϼ������仯����

???????·?????Tongji-GRACE01?????????????????????????????????????????????+??????????????????????????GIA????????????仯??????????GIA??????????????仯??????о???????RMS????2003-01??2011-08????????仯???????????-128.2±34.6 Gt/a??Pau-5-AUT??????-177.9±40.2 Gt/a??W&W-4-AUT??????-92.8±31.2 Gt/a??W12a??????     

贝叶斯正则化的Elman神经网络电离层TEC预报模型

利用2017年中低纬电离层总电子含量、地磁活动指数、年积日等参数,首次建立基于贝叶斯正则化(Bayesian regularization)的Elman回归神经网络(BR-Elman)的电离层TEC预报模型。同时,根据地磁活动指数的变化特征,分别进行平静电离层和扰动电离层预报建模。实验结果表明,该方法在平静期5 d预测值的均方根误差为1.19 TECu,残差为1.03 TECu,相关系数为0.93;在扰动期5 d预测值均方根误差为1.34 TECu,残差为1.01 TECu,相关系数为0.91。贝叶斯正则化的BP神经网络模型以及传统BP神经网络模型在平静期与扰动期5 d的预测上,均方根误差最小为1.87 TECu,残差最小为1.50 TECu,相关系数最优为0.87。通过对比分析,该模型较其他2个模型的预报效果有明显改善。     

Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data —— A Case Study in Qinghai-Tibet Plateau

On the basis of artificial neural network （ANN） model, this paper presents an algorithm for inversing snow depth with use of AMSR-E （Advanced Microwave Scanning Radiometer-Earth Observing System （EOS）） dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference （SPD） algorithm, and temperature gradient （TG） algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.     

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???GPS???????????????????????????y?????????BP?????編??????????????????????6????????????????????????????????????6?????????????????????????????BP?????編???????????3????????????????????     

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??????????????BP??????????????????????????У?????????????????????????????????硣?????????????GPS?????????б?????,?????????????????????????????????????????????????????????????????????????????????????????????????     

基于ALR的Hopfield神经网络在高程拟合中的应用

运用具有正规化项的增广拉格朗日函数作为神经网络的能量函数,辅助二次曲面拟合,进一步探索Hopfield神经网络在高程拟合中的应用。实际算例表明,该方法可以大大提高神经网络的计算效率和可靠性。     

��������ӦKalman�˲���BP�����絼���е�Ӧ��

?????BP??????????????????Kalman????????????????????????????????????BP???????÷????????BP?????????????????????????????????????????????÷??????????????????????Ч??????     

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????GPS???????????ж??????BP?????????溯???????????????????????÷?Χ?????????????????????????????????С???????????????LSSVM??????????????????????LSSVM?????????????????С????????????????????С??????????С??????????в?????????????????????????????????????????????LSSVM????????????????????????????????????????????????????????????????LSSVM?????????GPS???????????     

粒子群神经网络及其在非线性系统辨识中的应用

介绍了一种新的神经网络权值优化算法——粒子群优（Particle Swarm Optimization，PSO）算法，提出了用粒子群神经网络对非线性系统进行系统辨识的构思。仿真实验结果表明，粒子群算法具有比BP算法更强的非线性系统辨识能力和更好的泛化能力。     

人工神经网络和决策树模型在滑坡易发性分析中的性能对比

机器学习模型广泛应用于区域性滑坡易发性分析。模型的选择关系到评价结果的可信度、准确率和稳定性。现有滑坡易发性分析模型对比研究侧重模型的预测精度。模型的稳定性和数据量敏感性对机器学习模型的性能评估同样非常重要。本文以福建省南平市蔡源流域为研究区,以四川省绵阳市北川县为验证区,从预测精度、稳定性和数据量敏感性3个方面深入对比BP（Back Propagation）人工神经网络模型和CART（Classification and Regression Tree）决策树模型在滑坡易发性分析中的效果,主要结论如下:① 在逐渐增加一定数量训练样本的过程中,BP人工神经网络模型预测精度的增长率更高。在蔡源流域内,当训练样本数量增加10 000时,BP人工神经网络模型的预测精度上升5.22%,CART决策树模型的预测精度上升2.11%。② BP人工神经网络的预测精度高于CART决策树模型,且较为稳定。在100组数据集上,BP人工神经网络模型验证集预测精度的均值和验证集滑坡样本预测精度的均值分别为81.60%和84.86%,高于CART决策树模型的72.97%和76.59%。与此同时,BP人工神经网络模型对应预测精度的标准差分别是0.32%和0.37%,小于CART决策树模型的0.35%和0.67%。③ BP人工神经网络模型分析的滑坡易发区相比CART决策树模型,更接近实际滑坡的空间分布。最后,北川县的验证实验也出现了相同的现象。