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Fast SL0 algorithm for 3D imaging using bistatic MIMO radar 期刊论文
IET SIGNAL PROCESSING, 2018, 卷号: 12, 页码: 1017-1022
作者:  Hu, Xiaowei;  Guo, Yiduo;  Ge, Qichao;  Su, Yutong
收藏  |  浏览/下载:3/0  |  提交时间:2019/11/19
Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks 期刊论文
Applied Energy, 2015, 卷号: 157, 页码: 183-194
作者:  Liu, Hui*;  Tian, Hong-Qi;  Liang, Xi-Feng;  Li, Yan-Fei
收藏  |  浏览/下载:27/0  |  提交时间:2019/12/03
ARIMA  Auto Regressive Integrated Moving Average  ANN  Artificial Neural Networks  KF  Kalman Filter  MSM  Markov Switching Model  PCA  Principal Component Analysis  AA  Apriori Algorithm  BT  Bayesian Theory  SBM  Structural Break Modeling  GMCM  Gaussian Mixture Copula Model  NWP  Numerical Weather Prediction  KSF  Kalman Short-term Filtering  HIRLAM  High Resolution Limited Area Model  BP  Back Propagation  RBF  Radial Basis Function  ALE  Adaptive Linear Element  MAS  Multiple Architecture System  MLR  Multiple Linear Regression  MLP  Multi-Layer Perceptron  RBF  Radial Basis Function  SVM  Support Vector Machine  ABA  Ada-boost Algorithm  PSO  Particle Swarm Optimization  FAC  First-order Adaptive Coefficient  SAC  Second-order Adaptive Coefficient  SAA  Seasonal Adjustment Algorithm  ESM  Exponential Smoothing Method  MFNN  Multi-layer Feed-forward Neural Networks  FRR  Fuzzy Rough Regression  ELM  Extreme Learning Machines  MM5  Fifth Generation Mesoscale Model  GNWP  Global Numerical Weather Prediction  EPA  Evolutionary Programming Algorithm  OFM  Organizing Feature Maps  WD  Wavelet Decomposition  WPD  Wavelet Packet Decomposition  NF  Neuro-Fuzzy  ANFIS  Adaptive Neuro-Fuzzy Inference Systems  PM  Persistent Model  UKF  Unscented Kalman Filter  SVR  Support Vector Regression  FEEMD  Fast Ensemble Empirical Mode Decomposition  HM  Hammerstein Model  AR  Auto Regressive  EMD  Empirical Mode Decomposition  SDA  Secondary Decomposition Algorithm  PRWM  Persistent Random Walk Model  IMFs  Intrinsic Mode Functions  EEMD  Ensemble Empirical Mode Decomposition  GD-BP  Gradient Descent Back Propagation  GDM-BP  Gradient Descent with Momentum Back Propagation  GD-ALR-BP  Gradient Descent with Adaptive Learning Rate Back Propagation  GDM-ALR-BP  Gradient Descent with Momentum and Adaptive Learning Rate Back Propagation  CG-BP-FRU  Conjugate Gradient Back Propagation with Fletcher-Reeves Updates  CG-BP-PR  Conjugate Gradient Back Propagation with Polak-Ribiére Update  CG-BP-PBR  Conjugate Gradient Back Propagation with Powell-Beale Restarts  SCG-BP  Scaled Conjugate Gradient Back Propagation  BFGS-BP  Broyden-Fletcher-Goldfarb-Shanno Back Propagation  OSS-BP  One Step Secant Back Propagation  LM-BP  Levenberg Marquardt Back Propagation  MAE  Mean Absolute Error  MAPE  Mean Absolute Percentage Error  RMSE  Root Mean Square Error  Wind speed forecasting  Secondary decomposition algorithm  Wavelet packet decomposition  Fast ensemble empirical mode decomposition  Elman neural networks  
基于向量流场节点的图像分割算法 期刊论文
2010, 2010
李启翮; 罗予频; 萧德云; LI Qi-he; LUO Yu-pin; XIAO De-yun
收藏  |  浏览/下载:3/0
Fast multipole BEM for the analysis of pressure vessel opening structures 期刊论文
2010, 2010
Wang Wei; Wang Haitao; Shen Shifei
收藏  |  浏览/下载:2/0
Uplink carrier offset tracking method for orthogonal frequency division multiple access systems 期刊论文
2010, 2010
Ni Zuyao; Kuang Linling; Lu Jianhua
收藏  |  浏览/下载:5/0
Fast multipole integral equation method for VLSI interconnect inductance calculation 会议论文
作者:  Wang, Xiaoli;  Luo, Xianjue
收藏  |  浏览/下载:2/0  |  提交时间:2019/12/18
Fast determination of total ginsenosides content in Ginseng powder by near infrared reflectance spectroscopy (EI CONFERENCE) 会议论文
ICO20: Biomedical Optics, August 21, 2005 - August 26, 2005, Changchun, China
Chen H.-C.; Chen X.-D.; Lu Y.-J.; Cao Z.-Q.
收藏  |  浏览/下载:18/0  |  提交时间:2013/03/25
Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm1880 nm and 2230nm-2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR)  principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm-2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.  


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