Study of Fast Prediction Method for Transonic Flutter Boundary Using Radial Basis Function

2019 ◽  
Vol 18 (0) ◽  
pp. 133-141
Author(s):  
Yuta SAWAKI ◽  
Yuichi KUYA ◽  
Keisuke SAWADA
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Prediction Method ◽  
Radial Basis ◽  
Transonic Flutter ◽  
Flutter Boundary ◽  
Fast Prediction

An Improved Prediction Method of SOC Based on the GA-RBF Neural Network

2014 ◽  
Vol 953-954 ◽  
pp. 800-805 ◽  
Author(s):  
Meng Di Liang ◽  
Tie Zhou Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Charged State ◽  
Improved Genetic Algorithm ◽  
State Prediction ◽  
Radial Basis

Concerning the prediction problems’accuracy of the state-of-charge(SOC) of the battery，this paper proposes a prediction method based on an improved genetic algorithm-radial basis function neural network for power battery charged state. The prediction method, based on intensity of information interaction and neural activity, adjusts the size of the neural network online and solves the problem that radial basis function neural network structure adjustment influences the accuracy of charged state prediction. The simulation results show that，compared with the method of radial basis function neural network based on genetic algorithm , the accuracy of charged state prediction is more stable and more precise.

Prediction method of thermal conductivity of nanofluids based on radial basis function

2019 ◽  
Vol 141 (2) ◽  
pp. 859-880 ◽  
Author(s):  
Songyuan Zhang ◽  
Zhong Ge ◽  
Xingxiang Fan ◽  
Hui Huang ◽  
Xiaobo Long
Keyword(s):  
Thermal Conductivity ◽  
Radial Basis Function ◽  
Basis Function ◽  
Prediction Method ◽  
Radial Basis

scholarly journals The Rapid Data-Driven Prediction Method of Coupled Fluid–Thermal–Structure for Hypersonic Vehicles

Aerospace ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 265
Author(s):  
Jing Liu ◽  
Meng Wang ◽  
Shu Li
Keyword(s):  
Radial Basis Function ◽  
Proper Orthogonal Decomposition ◽  
Basis Function ◽  
Thermal Structure ◽  
Prediction Method ◽  
Orthogonal Decomposition ◽  
Data Driven ◽  
Good Efficiency ◽  
Radial Basis ◽  
Proper Orthogonal

This work demonstrates the use of Latin Hypercube Sampling and Proper Orthogonal Decomposition in combination with a Radial Basis Function model to perform on vehicle prediction coupled fluid–thermal–structure. We regarded the Mach number, flight altitude and angle of attack as input parameters and established a rapid prediction model. The basic process of numerical simulation of the hypersonic vehicle coupled fluid–thermal–structure was studied to obtain the database of pressure coefficient, heat flux, structural temperature and structural stress as the sample data to train this prediction method. The prediction error was analyzed. The prediction results showed that the data-driven method proposed in this paper based on proper orthogonal decomposition and radial basis function could be used for predicting vehicle coupled fluid–thermal–structure with good efficiency.

scholarly journals Artificial Intelligence and Neural Network-Based Shooting Accuracy Prediction Analysis in Basketball

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongfei Li ◽  
Maolin Zhang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Prediction Method ◽  
Rbf Network ◽  
Radial Basis ◽  
Hidden Layer ◽  
Time Required

In order to improve the accuracy of shooting in basketball. A shooting accuracy prediction method based on the convergent improved resource allocating network (CIRAN) online radial basis function neural network (RBFNN) is proposed, and the RBFNN learning algorithm is improved. Through the collection of shooting motion images, feature point extraction, and edge contour feature extraction, the shooting motion trajectory is obtained. Using the online neural network based on the CIRAN learning algorithm to predict the accuracy of shooting, this method analyzes the radial basis function (RBF) network. Based on the RBF analysis, the number of network layers and the number of hidden layer neurons are adjusted and optimized. In order to improve the prediction accuracy of shooting in basketball, a method based on. Through the analysis, it can be known that the accuracy of both the traditional RBFNN and the CIRAN-based online neural network for the prediction of shooting accuracy is above 70%. The prediction accuracy of the online neural network for shooting is higher than that of the traditional one. This is mainly because the online update function of the learning algorithm can better adjust the corresponding structure with the development of the game and has a better generalization ability. In addition, because the CIRAN learning algorithm introduces the hidden layer neuron deletion strategy, its network structure is simpler than that of the traditional one, the number of hidden layer neurons is less, and the running time required is less, which can better meet the real-time requirements and provide a more scientific method for basketball training.

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