Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

2020 ◽  
Vol 34 (3) ◽  
Author(s):  
Stephen Ojo ◽  
Agbotiname Imoize ◽  
Daniel Alienyi
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Path Loss ◽  
Signal Propagation ◽  
Lte Networks ◽  
Radial Basis ◽  
Loss Prediction ◽  
Path Loss Prediction

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.

scholarly journals Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach

2016 ◽  
Vol 1 (2) ◽  
pp. 77-84
Author(s):  
Ogbeide K. O. ◽  
Eko Mwenrenren E. J.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Artificial Neural Network Model ◽  
Path Loss ◽  
Signal Propagation ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Edo State ◽  
Artificial Neural

The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.

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>

Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach

2016 ◽  
Vol 1 (2) ◽  
pp. 77-84
Author(s):  
Ogbeide K. O. ◽  
Eko Mwenrenren E. J
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Artificial Neural Network Model ◽  
Path Loss ◽  
Signal Propagation ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Edo State ◽  
Artificial Neural

The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.

scholarly journals Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA)

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mingxun Zhu ◽  
Zhigang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Algorithm Optimization ◽  
Change Mechanism ◽  
Image Prediction ◽  
Radial Basis ◽  
Hidden Layer

The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.

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