Errata: Subpixel mapping on remote sensing imagery using a prediction model combining wavelet transform and radial basis function neural network

2009 ◽  
Vol 3 (1) ◽  
pp. 030103
Author(s):  
Zhongyang Guo
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Wavelet Transform ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Remote Sensing Imagery ◽  
Model Combining ◽  
Radial Basis ◽  
Subpixel Mapping

The Research Based on RBF Neural Network in the Power of Prediction of Grain Depot

2012 ◽  
Vol 182-183 ◽  
pp. 1358-1361
Author(s):  
Le Xiao ◽  
Min Peng Hu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Electricity Consumption ◽  
Nonlinear Time Series ◽  
Modeling And Prediction ◽  
Radial Basis

According to the fact that the use of electricity in grain depot is nonlinear time series, the article introduces the prediction model of electricity based on Radial Basis Function Neural Network, and conducts the modeling and prediction by adopting the historical electricity consumption of a typical grain depot. As the result of simulation shows, the model obtains better forecasting results in grain depot electricity.

Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

Pedosphere ◽  
2009 ◽  
Vol 19 (2) ◽  
pp. 176-188 ◽  
Author(s):  
Xiao-Hua YANG ◽  
Fu-Min WANG ◽  
Jing-Feng HUANG ◽  
Jian-Wen WANG ◽  
Ren-Chao WANG ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Regression Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Biophysical Parameters ◽  
Radial Basis

Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network

2007 ◽  
Vol 8 (6) ◽  
pp. 883-895 ◽  
Author(s):  
Xiao-hua Yang ◽  
Jing-feng Huang ◽  
Jian-wen Wang ◽  
Xiu-zhen Wang ◽  
Zhan-yu Liu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Radial Basis Function ◽  
Basis Function ◽  
Biophysical Parameters ◽  
Radial Basis

scholarly journals Radial basis function neural network for head roll prediction modelling in a motion sickness study

2019 ◽  
Vol 15 (3) ◽  
pp. 1637 ◽  
Author(s):  
Sarah ‘Atifah Saruchi ◽  
Mohd Hatta Mohammed Ariff ◽  
Mohd Ibrahim Shapiai ◽  
Nurhaffizah Hassan ◽  
Nurbaiti Wahid ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Motion Sickness ◽  
Basis Function ◽  
Roll Angle ◽  
Lateral Acceleration ◽  
Subjective Vertical ◽  
Radial Basis ◽  
Head Roll

<span>Motion Sickness (MS) is the result of uneasy feelings that occurs when travelling. In MS mitigation studies, it is necessary to investigate and measure the occupant’s Motion Sickness Incidence (MSI) for analysis purposes. One way to mathematically calculate the MSI is by using a 6-DOF Subjective Vertical Conflict (SVC) model. This model utilises the information of the vehicle lateral acceleration and the occupant’s head roll angle to determine the MSI. The data of the lateral acceleration can be obtained by using a sensor. However, it is impractical to use a sensor to acquire the occupant’s head roll response. Therefore, this study presents the occupant’s head roll prediction model by using the Radial Basis Function Neural Network (RBFNN) method to estimate the actual head roll responses. The prediction model is modelled based on the correlation between lateral acceleration and head roll angle during curve driving. Experiments have been conducted to collect real naturalistic data for modelling purposes. The results show that the predicted responses from the model are similar with the real responses from the experiment. In future, it is expected that the prediction model will be useful in measuring the occupant’s MSI level by providing the estimated head roll responses.</span>

Sign in / Sign up

Export Citation Format

Share Document