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中南大学 [4]
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西安交通大学 [1]
遥感与数字地球研究所 [1]
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期刊论文 [7]
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Predicting drivers' direction sign reading reaction time using an integrated cognitive architecture
期刊论文
IET Intelligent Transport Systems, 2019, 卷号: 13, 期号: 4, 页码: 622-627
作者:
Deng, Chao
;
Cao, Shi
;
Wu, Chaozhong*
;
Lyu, Nengchao
收藏
  |  
浏览/下载:25/0
  |  
提交时间:2019/12/04
mean square error methods
queueing theory
human factors
cognition
driver information systems
dual-task conditions
driver performance
integrated cognitive architecture
sight distance design requirements
cognitive simulation models
modelling methods
single task conditions
drivers direction sign reading reaction time prediction
expressways
information volume
concurrent tasks
queueing network-adaptive control of thought rational cognitive architecture
QN-ACTR
production rules
driving simulator program
human data
road names
root mean square error
mean absolute percentage error
MAPE
RMSE
Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm
期刊论文
Energy Conversion and Management, 2018, 卷号: 156, 页码: 525-541
作者:
Liu, Hui
;
Duan, Zhu
;
Han, Feng-ze
;
Li, Yan-fei*
收藏
  |  
浏览/下载:40/0
  |  
提交时间:2019/12/03
NWP
numerical weather prediction
CS
Cuckoo search
FS
fuzzy system
WRF
weather research and forecasting
KF
Kalman filter
ARIMA
auto-regressive integrated moving average
ARCH
autoregressive conditional heteroskedasticity
ANN
artificial neural networks
SVM
support vector machine
CRO
Coral Reefs optimization algorithm
ELM
extreme learning machine
MFNN
multi-layer feed-forward neural network
SPSA
simultaneous perturbation stochastic approximation
HM
Hammerstein Model
AR
auto-regressive
AdaBoost
adaptive boosting
MLP
multilayer perceptron
DNN-MRT
deep neural network based meta regression and transfer learning
WD
wavelet decomposition
FEEMD
fast ensemble empirical mode decomposition
EMD
empirical mode decomposition
WPD
wavelet packet decomposition
SSA
singular spectrum analysis
BFGS
Broyden–Fletcher–Goldfarb–Shanno Quasi-Newton Back Propagation
LSSVM
least square support vector machine
PSOGSA
partial swarm optimization combined with gravitational search algorithm
FCM
fuzzy C-means
EEMD
ensemble empirical mode decomposition
SampEn
sample entropy
VMD
variational mode decomposition
MAdaBoost
Modified AdaBoost.RT
WF
wavelet filter
MAE
mean absolute error
MAPE
mean absolute percentage error
RMSE
root mean squared error
CWT
continuous wavelet transform
DWT
discrete wavelet transform
LMD
local mean decomposition
ADMM
alternate direction method of multipliers
Big multi-step wind speed forecasting
Wavelet decomposition
Variational mode decomposition
Sample entropy
Modified adaBoost.RT
Wavelet filter
Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks
期刊论文
Energy Conversion and Management, 2018, 卷号: 155, 页码: 188-200
作者:
Liu, Hui
;
Mi, Xiwei
;
Li, Yanfei*
收藏
  |  
浏览/下载:41/0
  |  
提交时间:2019/12/03
Wind speed forecasting
Wavelet packet decomposition
Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
Artificial Neural Network
ANN
Artificial Neural Network
AR
Auto Regressive
ARIMA
Auto Regressive Integrated Moving Average
BA
Bat Algorithm
BP
Back-propagation Neural Network
CEEMDAN
Complete Ensemble Empirical Mode Decomposition
CG
Conjugate Gradient
CLSFPA
Flower Pollination Algorithm with Chaotic Local Search
CNN
Convolutional Neural Network
CS
Compressive Sensing
CSA
Cuckoo Search Algorithm
EEMD
Ensemble Empirical Mode Decomposition
ESM
Exponential Smoothing Method
FA
Firefly Algorithm
FAC
First-order Adaptive Coefficient
GA
Genetic Algorithm
GRNN
General Regression Neural Network
HBSA
Hybrid Backtracking Search Algorithm
HWM
Holt-Winters Model
IMFs
Intrinsic Mode Functions
IS
Input parameter Selection
KF
Kalman filter
LSSVM
Least Square Support Vector Machine
MAE
Mean Absolute Error
MAPE
Mean Absolute Percentage Error
MLP
Multilayer Perceptron Neural Network
MOBA
Multi Objective Bat Algorithm
NNCT
No Negative Constraint Theory
NSGA
Non-dominated Sorting Genetic Algorithm
OVMD
Optimized Variational Mode Decomposition
PSO
Particle Swam Optimization
PSOSA
Particle Swarm Optimization based on Simulated Annealing
PSR
Phase Space Reconstruction
RBF
Radial Basis Function Neural Network
RMSE
Root Mean Square Error
SAC
Second-order Adaptive Coefficient
SAM
Seasonal Adjustment Method
SDA
Secondary Decomposition Algorithm
SEA
Seasonal Exponential Adjustment
SOM
Self-Organizing feature Maps
SSA
Singular Spectrum Analysis
SVR
Support Vector Regression
VMD
Variational Mode Decomposition
v-SVM
v-Support Vector Machine
WD
Wavelet Decomposition
WPD
Wavelet Packet Decomposition
基于信噪比评价的阈值优选小波去噪法
期刊论文
2016, 2016
钟建军
;
宋健
;
由长喜
;
殷信桥
;
ZHONG Jianjun
;
SONG Jian
;
YOU Changxi
;
YIN Xinqiao
收藏
  |  
浏览/下载:7/0
Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms
期刊论文
Energy Conversion and Management, 2015, 卷号: 100, 页码: 16-22
作者:
Liu, Hui*
;
Tian, Hong-Qi
;
Li, Yan-Fei
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2019/12/03
WD
Wave let Decomposition
WPD
Wavelet Packet Decomposition
EMD
Empirical Mode Decomposition
EEMD
Ensemble Empirical Mode Decomposition
FEEMD
Fast Ensemble Empirical Mode Decomposition
MLP
Multi Layer Perceptron
ANFIS
Adaptive Network-based Fuzzy Inference System
BP
Back Propagation
GA
Genetic Algorithm
PSO
Particle Swarm Optimization
EA
Evolutionary Algorithm
AA
Adaboost Algorithm
ARIMA
Autoregressive Integrated Moving Average
SLFNN
Single-hidden Layer Feed-forward Neural Networks
MAE
Mean Absolute Error
MAPE
Mean Absolute Percentage Error
RMSE
Root Mean Square Error
Wind energy
Wind speed forecasting
Wind speed decomposition
Wavelet
Extreme learning machines
Empirical mode decomposition
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
Brain death prediction based on ensembled artificial neural networks in neurosurgical intensive care unit
期刊论文
Journal of the Taiwan Institute of Chemical Engineers, 2011, 卷号: 42, 期号: 1, 页码: 97-107
作者:
Liu, Quan
;
Cui, Xingran
;
Abbod, Maysam F.
;
Huang, Sheng-Jean
;
Han, Yin-Yi
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2019/12/04
ABP
arterial blood pressure
ANN
artificial neural network
ANNs
artificial neural networks
AUC
area under the ROC curve
BDI
brain death index
CPP
cerebral perfusion pressure
EANN
ensembled artificial neural network
ECG
electrocardiogram
EEG
electroencephalogram
EMD
empirical mode decomposition
EtCO2
end tidal of carbon dioxide
GCS
glasgow coma scale
ICP
intracranial pressure
ICU
intensive care unit
MLP
multilayer perceptron
MSE
mean square error
NHN
hidden nodes
NINP
the number of inputs
NOUT
the number of outputs
NPV
negative prediction value
NSC
National Science Council
NTUH
National Taiwan University Hospital
PPV
positive prediction value
RMSE
root mean square error
ROC
receiver operating characteristic
SEN
sensitivity
SPE
specificity
SpO2
oxygen in blood
Brain death prediction
Ensembled artificial neural networks
Sensitivity analysis
ROC curve analysis
Research and application of modeling for spacecraft TT&C ship swaying data
会议论文
作者:
Zhang, Zhonghua
;
Huang, Kai
;
Li, Xiaoyong
;
Yang, Lei
;
Chen, Guiming
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/12/18
Auto regressive models
Model application
Predicting method
Real time filtering
Research and application
Residual correlation
RMSE (root mean square error)
Time sequences
Estimation of Rock Mass Rating System with an Artificial Neural Network
会议论文
作者:
Zhang, Zhi Qiang
;
Wu, Qing Ming
;
Zhang, Qiang
;
Gong, Zhi Chao
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2019/12/05
Rock mass rating (RMR)
artificial neural network (ANN)
tunnel boring machine (TBM)
root mean square error (RMSE)
Remote chlorophyll-a retrieval in eutrophic inland waters by concentration classification Taihu Lake case study
会议论文
International Conference on Earth Observation Data Processing and Analysis, ICEODPA,, Wuhan, China, December 28, 2008 - December 30,2008
Du, Cong
;
Wang, Shixin
;
Zhou, Yi
;
Yan, Fuli
收藏
  |  
浏览/下载:20/0
  |  
提交时间:2014/12/07
In order to improve the precision of phytoplankton chlorophyll-a (chla) concentration retrieval
this study classified the data into two groups (the high and the low) by chla concentration with the threshold of 50gA&bullL-1. And then build the statistical models for each group. Particularly
a modifying factor OSS/TSS was used to unmixing the spectra in the low model to improve the low relationship between spectral reflectance and chla concentrations. As a result
the concentration classification model allowed estimation of chla with a root mean square error (RMSE) of 21.12gA&bullL-1 and the determination coefficient (R2) was 0.92
comparing with RMSE of chla estimation was 35.72gA&bullL-1 and R2=0.72 in the traditional model. It shows that concentration classification is a helpful method for accurate remote chla retrieval in eutrophic inland waters. 2008 SPIE.
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