An Approach of Non-stationary Harmonics Decomposition Based on Operator Approximated by Radial Base Function

2019 ◽  
pp. 539-548
Author(s):  
Ye Zeng ◽  
Qunjing Wang
Keyword(s):  
Base Function ◽  
Radial Base Function

Faults Classification for Transmission Line using Wavelet and Radial Base Function

2012 ◽  
Vol 132 (12) ◽  
pp. 936-941 ◽  
Author(s):  
Salma Abdel-Aal Shaaban ◽  
Takashi Hiyama
Keyword(s):  
Transmission Line ◽  
Base Function ◽  
Radial Base Function

scholarly journals Predicting construction labor productivity using lower upper decomposition radial base function neural network

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Sasan Golnaraghi ◽  
Osama Moselhi ◽  
Sabah Alkass ◽  
Zahra Zangenehmadar
Keyword(s):  
Neural Network ◽  
Labor Productivity ◽  
Base Function ◽  
Lower Upper ◽  
Radial Base Function

Applying RBF Neural Networks and Genetic Algorithms to Nonlinear System Optimization

2012 ◽  
Vol 605-607 ◽  
pp. 2457-2460 ◽  
Author(s):  
Hong Fa Wang ◽  
Xin Ai Xu
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Rbf Neural Network ◽  
System Optimization ◽  
Rbf Neural Networks ◽  
Optimal Solutions ◽  
Base Function ◽  
The Neural Network ◽  
Engineering Practices ◽  
Radial Base Function

Nonlinear system optimization is always an issue that needs to be considered in engineering practices and management. In order to obtain optimal solutions without analysis formulas to nonlinear systems, we first construct a radial-base-function (RBF) neural network using the newrb() function in MALTAB 7.0, then train the neural network according to input and output, and finally obtain the solution using a genetic algorithm. Simulated experimental results show that the proposed algorithm is able to achieve optimal solutions with a relatively fast speed of convergence.

ADAPTIVE BACKSTEPPING SLIDING MODE CONTROLLER FOR FLIGHT SIMULATOR BASED ON RBFNN

2013 ◽  
Vol 04 (04) ◽  
pp. 1342007 ◽  
Author(s):  
YUNJIE WU ◽  
BAITING LIU ◽  
WULONG ZHANG ◽  
XIAODONG LIU
Keyword(s):  
Controller Design ◽  
Sliding Mode ◽  
Flight Simulator ◽  
Sliding Mode Controller ◽  
Control Input ◽  
Adaptive Backstepping ◽  
External Disturbances ◽  
Base Function ◽  
System A ◽  
Radial Base Function

For flight simulator system, a kind of Adaptive Backstepping Sliding Mode Controller (ABSMC) based on Radial Base Function Neural Network (RBFNN) observer is presented. The sliding mode control theory is famous by its characteristic that it is insensitive to the external disturbances and parameters uncertainties. Combining this characteristic with Backstepping method can simplifies the controller design. And the addition of the terminal attractor can make the arrival time shorten greatly. However, too large external disturbances and parameters uncertainties are still not allowed to the system, and the design process of ABSMC does not have the upper bound information of disturbance until a RBFNN observer is designed to solve the problems. The simulation results show that the proposed scheme can improve the tracking precision and reduce the chattering of the control input, and the system has a higher robustness.

scholarly journals Analysis free surface of nonlinear seepage using the MQRBF method

2021 ◽  
Vol 233 ◽  
pp. 03042
Author(s):  
Yan SU ◽  
Yan SU ◽  
Zhi-ming ZHENG ◽  
Cheng-yu GU ◽  
Long-teng ZHANG
Keyword(s):  
Free Surface ◽  
Numerical Solution ◽  
Solution Process ◽  
Earth Dam ◽  
Richards Equation ◽  
Boundary Values ◽  
Base Function ◽  
Trefftz Method ◽  
Transient Problem ◽  
Radial Base Function

In order to solve the characteristics of low accuracy and slow efficiency in traditional numerical solution the free surface problem, the multiquardatic radial base function collocation method(MQ RBF) is used to analyze the constant seepage and unsteady seepage of the homogeneous earth dam. Computation of transient problem of free surface of earth dam by the linear derivation of Richards equation. The results show that the calculation accuracy of the MQRBF is higher than that of the traditional numerical method. The solution process does not involve numerical integral calculation and grid reorganization, which greatly reduces the calculation amount. Compared with the Trefftz method, it has the advantage of solving boundary values and internal values at the same time. It is not limited by the solution of the Laplace equation, and its application is wider and simpler.

A Hybrid Fuzzy-RBFN Filter for Data Classification

2015 ◽  
Vol 1117 ◽  
pp. 261-264 ◽  
Author(s):  
Balázs Tusor ◽  
Annamária R. Várkonyi-Kóczy
Keyword(s):  
Fuzzy Control ◽  
Control Systems ◽  
Data Classification ◽  
Clustering Methods ◽  
Base Function ◽  
Output Layer ◽  
Suggested Structure ◽  
Radial Base Function

In this paper, a new filter network is presented that is based on Radial Base Function Networks (RBFNs). The output layer of the network is modified, in order to make it more effective in certain fuzzy control systems. The training of the network is solved by a clustering step, for which two different clustering methods are proposed. The suggested structure can efficiently be used for data classification.

A New Support Vector Machine and Artificial Neural Networks for Prediction of Stuck Pipe in Drilling of Oil Fields

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Habib Rostami ◽  
Abbas Khaksar Manshad
Keyword(s):  
Support Vector Machine ◽  
Rbf Neural Network ◽  
Drilling Fluid ◽  
Oil Fields ◽  
Support Vector ◽  
Base Function ◽  
Rock Formations ◽  
Svm Model ◽  
Fluid Properties ◽  
Radial Base Function

Stuck pipe is known to be influenced by drilling fluid properties and other parameters, such as the characteristics of rock formations. In this paper, we develop a support-vector-machine (SVM) based model to predict stuck pipe during drilling design and operations. To develop the model, we use a dataset, including stuck and nonstuck cases. In addition, we develop radial-base-function (RBF) neural network based model, using the same dataset, and compare its results with the SVM model. The results show that the performance of both models for prediction of stuck pipe does not differ significantly and both of them have highly accurate and can be used as the heart of an expert system to support drilling design and operations.

scholarly journals Performance of multi layer feedforward and radial base function neural networks in classification and modelling

1996 ◽  
Vol 33 (2) ◽  
pp. 101-119 ◽  
Author(s):  
M.S. Sánchez ◽  
H. Swierenga ◽  
L.A. Sarabia ◽  
E. Derks ◽  
L. Buydens
Keyword(s):  
Neural Networks ◽  
Base Function ◽  
Radial Base Function
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