Radial basis function and its application in tourism management

In this work, several applications and the performances of the radial basis function (RBF) are briefly reviewed at first. After that, the binomial function combined with three different RBFs including the multiquadric (MQ), inverse quadric (IQ) and inverse multiquadric (IMQ) distributions are adopted to model the tourism data of Huangshan in China. Simulation results showed that all the models match very well with the sample data. It is found that among the three models, the IMQ-RBF model is more suitable for forecasting the tourist flow.

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Brain Tumor Classification by EGSO Based RBFNN Classifier

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6073.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 3005-3012

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Radial Basis Function Network ◽

Mri Brain ◽

Radial Basis ◽

The Mean ◽

Simulation Results ◽

Group Search ◽

Function Network ◽

The Ideal

Tumor classifier is modelled employing a proposed Enhanced Group Search Optimizer based Radial Basis Function Neural Network model is applied in this research contribution to acquire the ideal instances from the developed VOI instance an as well EGSO is utilized to optimize the weight values of the Radial Basis Function Network classifier by limiting the mean square mistake. The anticipated EGSO based RBFNN classifier brings better characterization precision and accomplished insignificant error with quicker process. The simulation results computed prove the effectiveness of the RBFNN classifier to be better in comparison with the other proposed classifiers in this thesis and that available in the literature. The proposed pattern evaluation technique presents an automatic cancer categorization procedure thru the ultimate facets which fantastic characterizes MRI brain image is benign and malignant cancers. The planned method may perhaps stretch to categorize exceptional classes of tumor (eg. Meningioma, glioma etc.,) and depth of malignancy.

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Forecasting Consumer Confidence Index Based on Radial Basis Function Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.200 ◽

2014 ◽

Vol 596 ◽

pp. 200-203

Author(s):

Hai Yan Xie ◽

Guo Yan Chen ◽

Zhen He ◽

De Peng Zhao

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Rbf Neural Network ◽

Point Of View ◽

Consumer Confidence ◽

Bp Network ◽

Radial Basis ◽

Simulation Results ◽

Confidence Index

Consumer confidence index (CCI) is a basic economic parameter of country economic development changes and is observed in the point of view of consumers. To predict the CCI for the next few months, Radial basis function (RBF) neural network is introduced. Compared with the BP network, the simulation results obtained by RBF make more accurate precisions with better fitting effects. From the results of our prediction, the analysis for the tendency of CCI in the future is also obtained.

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Cutting Process Model Design of Cutter Suction Dredger Based on Auto Regressive eXogenous and Radial Basis Function model

Journal of Physics Conference Series ◽

10.1088/1742-6596/2137/1/012064 ◽

2021 ◽

Vol 2137 (1) ◽

pp. 012064

Author(s):

Huan Zhang ◽

Menghong Yu ◽

Wei Yuan

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Process Model ◽

Cutting Process ◽

Model Design ◽

Arx Model ◽

Radial Basis ◽

Auto Regressive ◽

Simulation Results ◽

The Mathematical Model

Abstract The dredging operation of the strander dredger is complex, and the mathematical model established according to its key equipment characteristics is not possible to describe such a system having time degeneration and non-linear. Therefore, based on the analysis of mud formation process of dredger, RBF-ARX model is used to model the cutting process, and mud concentration is taken as the output. This modeling method is a combination model based on the theory of Auto-Regressive eXogenous (ARX) model and Gauss radial basis function (Radial Basis Function) neural network (RBF). The comparison between the simulation results and the actual data shows that the model can accurately describe the dynamic characteristics of cutter suction dredger in the cutting process.

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