scholarly journals Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhen Li ◽  
Jianping Hao ◽  
Cuijuan Gao
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Effectiveness Evaluation ◽  
Generative Adversarial Network ◽  
Generalization Ability ◽  
Rbf Network ◽  
Adversarial Network ◽  
Radial Basis ◽  
Network Evaluation

Due to the lack of maintenance support samples, maintenance support effectiveness evaluation based on the deep neural network often faces the problem of small sample overfitting and low generalization ability. In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. It adds category constraint based on category probability vector reordering function to GAN loss function, avoids the simplification of generated sample categories, and enhances the quality of generated samples. It also designs a parameter initialization method based on parameter components equidistant variation for RBF network, which enhances the response of correct feature information and reduces the risk of training overfitting. The comparison results show that the mean square error (MSE) of the improved GAN-RBF model is 5.921 × 10 − 4 , which is approximately 1/2 of the RBF model, 1/3 of the Elman model, and 1/5 of the BP model, while its complexity remains at a reasonable level. Compared with traditional neural network evaluation methods, the improved GAN-RBF model has higher evaluation accuracy, better solves the problem of poor generalization ability caused by insufficient training samples, and can be more effectively applied to maintenance support effectiveness evaluation. At the same time, it also provides a good reference for evaluation research in other fields.

scholarly journals A Novel Radial Basis Function Neural Network with High Generalization Performance for Nonlinear Process Modelling

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 140
Author(s):  
Yanxia Yang ◽  
Pu Wang ◽  
Xuejin Gao
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Error Bound ◽  
Basis Function ◽  
Nonlinear Process ◽  
Generalization Error ◽  
Sample Mean ◽  
Generalization Ability ◽  
Radial Basis ◽  
Generalization Error Bound

A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it very difficult to construct an optimal network structure to ensure the generalization ability of the established nonlinear process model. To solve this problem, a novel RBFNN with a high generation performance (RBFNN-GP), is proposed in this paper. The proposed RBFNN-GP consists of three contributions. First, a local generalization error bound, introducing the sample mean and variance, is developed to acquire a small error bound to reduce the range of error. Second, the self-organizing structure method, based on a generalization error bound and network sensitivity, is established to obtain a suitable number of neurons to improve the generalization ability. Third, the convergence of this proposed RBFNN-GP is proved theoretically in the case of structure fixation and structure adjustment. Finally, the performance of the proposed RBFNN-GP is compared with some popular algorithms, using two numerical simulations and a practical application. The comparison results verified the effectiveness of RBFNN-GP.

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.

Radial Basis Function Networks for Analog Circuit Fault Isolation

1997 ◽  
Vol 07 (06) ◽  
pp. 643-655 ◽  
Author(s):  
N. S. C. Babu ◽  
V. C. Prasad
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Analog Circuits ◽  
Basis Function ◽  
Analog Circuit ◽  
Selection Procedure ◽  
Fault Isolation ◽  
Fault Dictionary ◽  
Rbf Network ◽  
Radial Basis

The application of a radial basis function neural network (RBFN) for analog circuit fault isolation is presented. In this method the RBFN replaces the fault dictionary of analog circuits. The proposed method for analog circuit fault isolation takes the advantage of extremely fast training of RBFN compared to earlier neural network methods. A method is suggested to select centers and widths of RBF units. This selection procedure accounts for the component tolerances. The effectiveness of the RBFN for the fault isolation problem is demonstrated with an illustrative example. RBFN performed well even when the input patterns are drawn directly from the test node voltages of the analog circuit under consideration. A method is suggested to modify the RBF network in the event of occurrence of a new fault. The suggested modifications do not affect the previous training.

Nature Frequency Identification of Prestressed Concrete Beam with RBF Network

2012 ◽  
Vol 446-449 ◽  
pp. 362-365 ◽  
Author(s):  
Rui Ge Li ◽  
Guo Li Yang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Test Data ◽  
Basis Function ◽  
Prestressed Concrete ◽  
Concrete Beam ◽  
Test Beam ◽  
Rbf Network ◽  
Prestressed Concrete Beam ◽  
Radial Basis

Abstract. The relationship of pre-stressed concrete (PSC) beam natural frequency and pre-stressed force is difficult to described accurately with mechanical model. The past experimental data are collected. Then five unbonded post-tensioned PSC beams are designed. Frequencies and damps are collected in the dynamic experiment of five PSC beams. Radial basis function neural network is constructed to identify the natural frequencies of prestressed beam with different levels prestressing force based on previous test data and new dynamic test beam data. Then the input and output node numbers of neural network are selected and the appropriate training algorithm and expansion coefficient is determined. In order to verify that the network performance, one prestressed concrete beam test data are left to simulation test. Simulation results show that the radial basis function neural network is feasibility to recognize the frequency of PSC beams.

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.

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.

Heat Transfer Prediction for Helical Baffle Heat Exchangers with Experimental Data by Radial Basis Function Neural Networks

2012 ◽  
Vol 452-453 ◽  
pp. 1441-1445 ◽  
Author(s):  
Bin Jiang ◽  
Xiao Ling Yang ◽  
Lu Hong Zhang
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Radial Basis Function ◽  
Basis Function ◽  
Heat Exchangers ◽  
Volumetric Flow Rate ◽  
Rbf Network ◽  
Radial Basis ◽  
Helical Baffle

In this paper an artificial neural network (ANN) is used to correlate experimentally determined heat transfer rate of non-continuous helical baffle heat exchangers. First the heat exchangers with three helical angles were experimentally investigated under different inlet volumetric flow rate and temperature. The commonly implemented radial-basis function (RBF) neural network is applied to develop a prediction model based on the limited experimental data. Compared with correlations, the RBF network exhibits superiority in accuracy. The satisfactory results suggest the RBF network might be used to predict the thermal performance of shell-and-tube heat exchangers with helical baffles.

Radial basis function neural networks for reliably forecasting rainfall

2012 ◽  
Vol 3 (2) ◽  
pp. 125-138 ◽  
Author(s):  
Amr H. El Shafie ◽  
A. El-Shafie ◽  
A. Almukhtar ◽  
Mohd. R. Taha ◽  
Hasan G. El Mazoghi ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Significant Feature ◽  
Hydrological Process ◽  
Rainfall Forecasting ◽  
Rbf Network ◽  
Analysis And Design ◽  
Linear Relationships ◽  
Radial Basis

Rainfall forecasting is an interesting task especially in a modern city facing the problem of global warming; in addition rainfall is a necessary input for the analysis and design of hydrologic systems. Most rainfall real-time forecasting models are based on conceptual models simulating the complex hydrological process under climate variability. As there are a lot of variables and parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically based models is often a difficult and time-consuming procedure. Simpler artificial neural network (ANN) forecasts may therefore seem attractive as an alternative model. The present research demonstrates the application of the radial basis function neural network (RBFNN) to rainfall forecasting for Alexandria City, Egypt. A significant feature of the input construction of the RBF network is based on the use of the average 10 year rainfall in each decade to forecast the next year. The results show the capability of the RBF network in forecasting the yearly rainfall and two highest rainfall monsoon months, January and December, compared with other statistical models. Based on these results, the use of the RBF model can be recommended as a viable alternative for forecasting the rainfall based on historical rainfall recorded data.

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