Shunt Active Filter Based on Radial Basis Function Neural Network and p-q Power Theory

2017 ◽  
Vol 8 (2) ◽  
pp. 667 ◽  
Author(s):  
Prakash Ch. Tah ◽  
Anup K. Panda ◽  
Bibhu P. Panigrahi
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
New Combination ◽  
Training Methods ◽  
Power Theory ◽  
Rbf Network ◽  
Radial Basis

In this paper a new combination Radial Basis Function Neural Network and p-q Power Theory (RBFNN-PQ) proposed to control shunt active power filters (SAPF). The recommended system has better specifications in comparison with other control methods. In the proposed combination an RBF neural network is employed to extract compensation reference current when supply voltages are distorted and/or unbalance sinusoidal. In order to make the employed model much simpler and tighter an adaptive algorithm for RBF network is proposed. The proposed RBFNN filtering algorithm is based on efficient  training methods called hybrid learning method.The method  requires a small size network, very robust, and the proposed algorithms are very effective. Extensive simulations are carried out with PI as well as RBFNN controller for p-q control strategies by considering different voltage conditions and adequate results were presented.

The Study of Precipitation Forecast Model on EMD-RBF Neural Network - A Case Study on Northeast China

2014 ◽  
Vol 641-642 ◽  
pp. 119-122 ◽  
Author(s):  
Xiao Sun ◽  
Shi Fan Qiao ◽  
Ji Ren Xie
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Precipitation Forecast ◽  
Maximum Relative Error ◽  
Precipitation Data ◽  
Rbf Network ◽  
Precipitation Forecasting ◽  
Radial Basis

Based on the principal of forecast of Artificial Neural Network, Radial Basis Function neural network and Radial Basis Function neural network based on EMD were introduced into the field of precipitation forecasting in this article. With the precipitation data of 27 sites from1950-2010, EMD-RBF network was set up, and the difference between the predictive value and the actual precipitation data was discussed. The results showed that the correlation Of EMD-RBF forecast precipitation and actual precipitation is more than 0.9. Of all sites, the maximum relative prediction error of 17 sites is less than 10%, the maximum relative error does not exceed 15%.The EMD-RBF model had good quality on forecasting precision, which provided a new method for precipitation forecasting.

scholarly journals Rainfall Runoff Modeling using Radial Basis Function Neural Network for Sungai Tinjar Catchment, Miri, Sarawak

2009 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Suhaimi S. ◽  
Rosmina A. Bustami
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Rainfall Runoff ◽  
Optimum Result ◽  
Rbf Network ◽  
Linear Relationships ◽  
Radial Basis ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) is a very useful data modelling tool that is able to capture and represent complex input and output relationships. The advantage of ANN lies in its ability to represent both linear and non-linear relationships and in its ability to learn these relationships directly from the data being modelled. Modeling of rainfall runoff relationship is important in view of the many uses of water resources such as hydropower generation, irrigation, water supply and flood control.This study is to purposefully develop a rainfall runoff model for Sg. Tinjar with outlet at Long Jegan using Radial Basis Function (RBF) Neural Network. Training and simulation was done using Matlab 6.5.1 software with varying parameters to obtain the optimum result. Further, the results were compared to simulation done with Multilayer Percepteron model. The RBF network developed in this study has successfully modelled rainfall runoff relationship in Sungai Tinjar Catchment in Miri, Sarawak with an accuracy of about 98.3%.

Direct Adaptive Control for a Certain Class of Nonlinear Systems Using Modified Radial Basis Function Neural Network

Volume 1 ◽  
2004 ◽  
Author(s):  
Hsuan-Ju Chen ◽  
Rongshun Chen
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Controller Design ◽  
Rbf Neural Network ◽  
Design Parameters ◽  
State Variables ◽  
Rbf Network ◽  
Radial Basis

This paper proposes a direct adaptive controller for SISO affine nonlinear systems using Gaussian radial basis function (RBF) neural network (NN). The exact plant model is not necessary for composing the controller. If the plant is SISO, of affine form, without zero dynamics, and all the state variables are available, the controller is applicable under several mild assumptions. In this paper, the Gaussian RBF network (GRBFN) is modified to include pre-scale weights as its parameters for the input variables, which are also adapted in the control law. Pre-scaling the inputs is equivalent to extending or contracting the spectrum of the approximated function. With the modification, the spectrum along each coordinate of the domain can be scaled separately for approximating. The adaptation of the nonlinear parameters, including the variances, centers, and pre-scaling weights, are derived. Appropriate modification techniques are applied to the adaptation laws to ensure the robustness. The stability is analyzed with Lyapunov’s Theory. From the analysis, the effect of the controller design parameters is also examined. A simulation of an inverted pendulum control is demonstrated to show the effectiveness.

Research on Intelligent Diagnosis Technology of Transformer Fault

2013 ◽  
Vol 385-386 ◽  
pp. 589-592
Author(s):  
Hong Qi Wu ◽  
Xiao Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Intelligent Diagnosis ◽  
Rbf Network ◽  
Working Principle ◽  
Research On Application ◽  
Transformer Fault

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

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 A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

2019 ◽  
Vol 11 (21) ◽  
pp. 6125
Author(s):  
Lianyan Li ◽  
Xiaobin Ren
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Smart Growth ◽  
Principal Component Regression ◽  
Principal Component ◽  
Self Organizing Map ◽  
Radial Basis

Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth.

scholarly journals Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Wei Liu ◽  
Feifan Wang ◽  
Xiawei Yang ◽  
Wenya Li
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Radial Basis Function ◽  
Basis Function ◽  
Welded Joints ◽  
Friction Welding ◽  
Rbf Neural Network ◽  
Finite Element Simulations ◽  
Welding Parameters ◽  
Radial Basis

This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW), a radial basis function (RBF) neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW) and continuous drive friction welding (CDFW). The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

scholarly journals Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

2020 ◽  
Vol 8 (3) ◽  
pp. 210 ◽  
Author(s):  
Renqiang Wang ◽  
Donglou Li ◽  
Keyin Miao
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Radial Basis Function ◽  
Intelligent Control ◽  
Basis Function ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Unmanned Surface Vehicles ◽  
Radial Basis

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts.

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