The Dynamic Grey Radial Basis Function Prediction Model and its Applications

2006 ◽  
Author(s):  
Jingling Yuan ◽  
Luo Zhong ◽  
Yang Yu
Keyword(s):  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Function Prediction ◽  
Radial Basis

scholarly journals Coking energy consumption radial basis function prediction model improved by differential evolution algorithm

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1122-1130 ◽  
Author(s):  
Wenhua Tao ◽  
Jiao Chen ◽  
Yunjin Gui ◽  
Pingping Kong
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Differential Evolution Algorithm ◽  
Function Prediction ◽  
Radial Basis ◽  
Evolution Algorithm

This paper presents a radial basis function prediction model improved by differential evolution algorithm for coking energy consumption process, which is very difficult to get online and real time because of the complex process. In the energy consumption prediction model, target flue temperature, flue suction, water content, volatile coal and coking time are considered as input variables, and coking energy consumption as output variables. To overcome the shortcomings of radial basis function network, such as poor learning ability and slow convergence speed, the energy consumption prediction model optimized by differential evolution algorithm is improved. Using the strong global search ability of differential evolution algorithm, the center value, width and output weight of the basis function in radial basis function network is obtained by differential evolution algorithm. Then the optimal values are taken as the center value, width and output weight of the of radial basis function neural network. The results show that the improved radial basis function prediction has higher accuracy, stability and training speed of the network. The radial basis function prediction model has great significance in reducing coking energy consumption, saving enterprise costs, increasing coke production and improving enterprise economic benefits.

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.

Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 53-75 ◽  
Author(s):  
Mrutyunjaya Panda
Keyword(s):  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rapid Development ◽  
Defect Prediction ◽  
Instance Selection ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Radial Basis ◽  
Base Balance

Software is an important part of human life and with the rapid development of software engineering the demands for software to be reliable with low defects is increasingly pressing. The building of a software defect prediction model is proposed in this article by using various software metrics with publicly available historical software defect datasets collected from several projects. Such a prediction model can enable the software engineers to take proactive actions in enhancing software quality from the early stages of the software development cycle. This article introduces a hybrid classification method (DBBRBF) by combining distribution base balance (DBB) based instance selection and radial basis function (RBF) neural network classifier to obtain the best prediction compared to the existing research. The experimental results with post-hoc statistical significance tests shows the effectiveness of the proposed approach.

The Prediction Model of Macro-Road Traffic Accident Basing on Radial Basis Function

2011 ◽  
Vol 97-98 ◽  
pp. 981-984 ◽  
Author(s):  
Cheng Ju Song ◽  
Quan Yan Li
Keyword(s):  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Prediction Models ◽  
Road Traffic ◽  
Accident Prediction ◽  
Radial Basis ◽  
Traffic Operation

The Macro-road traffic accident prediction is an important branch of ITS, which could not only make improving direction, but also improve the traffic operation. The paper based on the analyzing the existing macro prediction model, aiming at the existing shortcomings of prediction models with low accuracy and slow convergence speed, introducing the Radial Basis Function, establishing the accident prediction model between Population, Economic situation, cars, road mileage and the index of accident Statistics, and apply Matlab to Simulation to prove the Feasibility and Practicality of the model.

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>

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