Design of RBF Neural Networks Based on Adjustable Radius

2010 ◽  
Vol 439-440 ◽  
pp. 605-610
Author(s):  
Xiao Yong Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Function Approximation ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Gaussian Function ◽  
Mean Square ◽  
Rbf Neural Networks ◽  
Data Points ◽  
Better Than

In this paper, a new RBF neural network (RBFNN) algorithm, called ar-RBFNN, is presented. In traditional RBFNNs based on clustering algorithm, called oRBFNN in this paper, the width of the basis function-Gaussian function, or called radius, ignored the effect of numbers in different clusters, or density of data points. New algorithm considers radius is effect to performance of algorithms in problem of function approximation. Mean Square Error is used to evaluate performances of two algorithms, oRBFNN and ar-RBFNN algorithms. Several experiments in function approximation show ar-RBFNN is better than oRBFNN.

The Application of RBF Neural Networks in Curve Fitting

2012 ◽  
Vol 490-495 ◽  
pp. 688-692
Author(s):  
Zhong Biao Sheng ◽  
Xiao Rong Tong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Rbf Neural Network ◽  
Engineering Application ◽  
Nonlinear Function ◽  
Rbf Neural Networks ◽  
Neural Network Approach ◽  
Rbf Network ◽  
Fast Learning ◽  
Nonlinear Processing

Three means to realize function approach such as the interpolation approach, fitting approach as well as the neural network approach are discussed based on Matlab to meet the demand of data processing in engineering application. Based on basic principle of introduction, realization methods to non-linear are researched using interpolation function and fitting function in Matlab with example. It mainly studies the RBF neural networks and the training method. RBF neural network to proximate nonlinear function is designed and the desired effect is achieved through the training and simulation of network. As is shown from the simulation results, RBF network has strong nonlinear processing and approximating features, and RBF network model has the characteristics of high precision, fast learning speed for the prediction.

Research of Automotive Glass Fog System Based on RBF Neural Network

2012 ◽  
Vol 588-589 ◽  
pp. 1441-1445
Author(s):  
Liu Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Rbf Neural Network ◽  
Rbf Neural Networks ◽  
Matlab Software ◽  
New Type ◽  
New System

By describing a danger from driving vehicles with fog on windshield, we give a concept of a new type of automatic windshield defogging system applying traditional sensor and RBF neural networks. In terms of an analysis on the source of fogging on automatic windshield, applying traditional sensor, we design a RBF neural networks. Then, via RBF neural networks mode, training and testing 48 series of data from an experiment. A result of MATLAB software demonstrates that this new system defog from automatic windshield swiftly and precisely by applying RBF neural networks.

scholarly journals A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Jia Wang ◽  
Shenglong Zhang ◽  
Xia Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Rbf Neural Network ◽  
Lithium Ion ◽  
Rbf Neural Networks ◽  
Battery Packs ◽  
Diagnosis Method

With the increasing demand for electric vehicles, the high voltage safety of electric vehicles has attracted significant attention. More than 30% of electric vehicle accidents are caused by the battery system; hence, it is vital to investigate the fault diagnosis method of lithium-ion battery packs. The fault types of lithium-ion battery packs for electric vehicles are complex, and the treatment is cumbersome. This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural networks were employed to identify the fault of the lithium-ion battery pack system using the experimental data. The diagnosis test results showed that the improved RBF neural networks could effectively identify the fault diagnosis information of the lithium-ion battery packs, and the diagnosis accuracy was about 100%.

Study on LPG Air Fuel Ratio Based on Improved Subtractive Clustering RBF Neural Networks

2011 ◽  
Vol 474-476 ◽  
pp. 1122-1127
Author(s):  
Yong Man Lin ◽  
Zi Ping Feng ◽  
Hai Feng Guan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Gasoline Engine ◽  
Rbf Neural Network ◽  
Subtractive Clustering ◽  
Rbf Neural Networks ◽  
Liquefied Petroleum Gas ◽  
Engine Simulation ◽  
Engine Model ◽  
And Control

The paper refers to the mathematical model of gasoline engine, and builds liquid-jet LPG(Liquefied Petroleum Gas) engine model. Based on the model, when the specific of the parameters distribution of operating engine are known, RBF neural network can estimate center value and the number of hidden layers precisely, and control engine A/F in fine range. But the parameter features of operating engine are unknown in advance. The paper provides a improved subtractive clustering - RBF neural Networks algorithm to control A/F of LPG engine. Simulation shows, improved subtractive clustering can precisely determine the number of neuron of RBF neural network hidden layers under unknown operation parameters, and the precision is higher, and self-study and adaptive adjusting is better than before.

Stream Turbine Vibration Fault Diagnosis

2013 ◽  
Vol 340 ◽  
pp. 90-94 ◽  
Author(s):  
Hong Sheng Su
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Local Extremum ◽  
Decision Table ◽  
Rbf Neural Networks ◽  
Global Extremum ◽  
Artificial Fish Swarm Algorithm ◽  
Diagnosis Method

RBF neural networks possessed the excellent characteristics such as insensitive on the initial weights and parameters with artificial fish-swarm algorithm (AFSA) applied, which made it have abilities to get rid of the local extremum and obtain the global extremum, and called as AFSA-RBF neural networks. In this paper, a new stream turbine vibration fault diagnosis method was presented based on AFSA-RBF neural networks. After quantification and reduction of the diagnosis decision table, the simplified decision table served as the learning samples of AFSA-RBF neural network, and the well-trained neural network was then applied to diagnose stream turbine vibration faults. The diagnosis results show that the proposed method possesses higher convergence speed and diagnosis precision, and is a very effective turbine fault diagnosis method.

Modeling and Prediction of the CNY Exchange Rates Using RBF Neural Networks versus GARCH Models

2010 ◽  
Vol 39 ◽  
pp. 375-382 ◽  
Author(s):  
Zhao Cheng Liu ◽  
Xi Yu Liu ◽  
Zi Ran Zheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Exchange Rates ◽  
Transfer Functions ◽  
Rbf Neural Network ◽  
Network Models ◽  
Garch Models ◽  
Rbf Neural Networks ◽  
Neural Network Models

The CNY exchange rates can be viewed as financial time series which are characterized by high uncertainty, nonlinearity and time-varying behavior. Predictions for CNY exchange rates of GBP-CNY and USD-CNY were carried out respectively by means of RBF neural network forecasters and GARCH models. GARCH is a mechanism that includes past variances in the explanation of future variances and a time-series technique that we use to model the serial dependence of volatility. The detailed design of architectures of RBF neural network models, transfer functions of the hidden layer nodes, input vectors and output vectors were made with many tests. While experimental results show that the performance of RBF neural networks for forecasting spot CNY exchange rates is better than that of GARCH, both of them are acceptable and effective especially in short term predictions.

A Method about Load Distribution of Rolling Mills Based on RBF Neural Network

2011 ◽  
Vol 279 ◽  
pp. 418-422
Author(s):  
Dong Dong Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Load Distribution ◽  
Rolling Force ◽  
Rbf Neural Network ◽  
Rbf Neural Networks ◽  
Rolling Mills ◽  
The Neural Network ◽  
Network Output

Rolling mills process is too complicated to be described by formulas. RBF neural networks can establish finishing thickness and rolling force models. Traditional models are still useful to the neural network output. Compared with those finishing models which have or do not have traditional models as input, the importance of traditional models in application of neural networks is obvious. For improving the predictive precision, BP and RBF neural networks are established, and the result indicates that the model of load distribution based on RBF neural network is more accurate.

Prediction of Tensile Strength Based on RBF Neural Network

2012 ◽  
Vol 476-478 ◽  
pp. 1309-1312
Author(s):  
Hui Jun Li ◽  
Li Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tensile Strength ◽  
Prediction Models ◽  
Rbf Neural Network ◽  
Rbf Neural Networks ◽  
Test Results ◽  
Strength Based

The objective of this research is to predict yarn tensile strength. The model of predicting yarn tensile strength is built based on RBF neural network. The RBF neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on RBF neural network are very precise and efficient.

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