scholarly journals An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3091 ◽  
Author(s):  
Yipeng Ning ◽  
Jian Wang ◽  
Houzeng Han ◽  
Xinglong Tan ◽  
Tianjun Liu
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Radial Basis Function ◽  
Basis Function ◽  
Navigation System ◽  
Urban Areas ◽  
Rbf Neural Network ◽  
Integrated Systems ◽  
Model Errors ◽  
Robust Kalman Filter

Inertial Navigation System (INS) is often combined with Global Navigation Satellite System (GNSS) to increase the positioning accuracy and continuity. In complex urban environments, GNSS/INS integrated systems suffer not only from dynamical model errors but also GNSS observation gross errors. However, it is hard to distinguish dynamical model errors from observation gross errors because the observation residuals are affected by both of them in a loosely-coupled integrated navigation system. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors. In the proposed method, firstly a test statistic based on Mahalanobis distance is treated as judging index to achieve fault detection. Then, an optimal RBF neural network strategy is trained on-line by the optimality principle. The network’s output will bring benefits in recognizing the above two kinds of filtering fault and the system is able to choose a robust or adaptive Kalman filtering method autonomously. A field vehicle test in urban areas with a low-cost GNSS/INS integrated system indicates that two types of errors simulated in complex urban areas have been detected, distinguished and eliminated with the proposed scheme, success rate reached up to 92%. In particular, we also find that the novel neural network strategy can improve the overall position accuracy during GNSS signal short-term outages.

Research on Learning Algorithm of RBF Neural Network Based on Extended Kalman Filter

2014 ◽  
Vol 989-994 ◽  
pp. 2705-2708
Author(s):  
Xu Sheng Gan ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Radial Basis Function ◽  
Extended Kalman Filter ◽  
Basis Function ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Learning Capability ◽  
Radial Basis ◽  
Neural Network Algorithm

To improve the learning capability of Radial Basis Function (RBF) neural network, a RBF neural network algorithm based on Extended Kalman Filter (EKF) is proposed. First the basic idea of EKF algorithm and RBF neural network are introduced, and then EKF is used to optimize the parameters combination of RBF neural network to obtain the better model. The experiment proves its feasibility.

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.

Performance Assessment of Hydraulic Servo System Based on Radial Basis Function Neural Network and Mahalanobis Distance

2015 ◽  
Vol 764-765 ◽  
pp. 613-618
Author(s):  
Zhen Ya Wang ◽  
Chen Lu ◽  
Hong Mei Liu ◽  
Zi Han Chen
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Performance Assessment ◽  
Basis Function ◽  
Mahalanobis Distance ◽  
Rbf Neural Network ◽  
Residual Error ◽  
Servo Systems ◽  
Radial Basis ◽  
Hydraulic Servo

The performance assessment of hydraulic servo systems has attracted an increasing amount of attention in recent years. However, only a few studies have focused on practical approaches in this field. A performance assessment method based on radial basis function (RBF) neural network and Mahalanobis distance (MD) is proposed in this study; the method is quantized by the performance confidence value (CV). An observer model based on RBF neural network is designed to calculate the residual error between the actual and estimated outputs. The root mean square (RMS), peak value, and average absolute value are then extracted as the features of residual error, which serve as the coordinates of the feature points. Lastly, the MD between the most recent feature point and the constructed Mahalanobis space is calculated. The condition of the system is assessed by normalizing MD into a CV. The proposed method is proven to be effective by a simulation model in which leakage faults are injected. Experimental results show that the proposed method can assess the performance of hydraulic servo systems effectively.

scholarly journals Research on the Method of Methane Emission Prediction Using Improved Grey Radial Basis Function Neural Network Model

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6112
Author(s):  
Yongkang Yang ◽  
Qiaoyi Du ◽  
Chenlong Wang ◽  
Yu Bai
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Methane Emission ◽  
Basis Function ◽  
Simulation Experiment ◽  
Rbf Neural Network ◽  
Grey Theory

Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

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