scholarly journals Indoor Positioning of RBF Neural Network Based on Improved Fast Clustering Algorithm Combined With LM Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 5932-5945 ◽  
Author(s):  
Hao Meng ◽  
Fei Yuan ◽  
Tianhao Yan ◽  
Mingfang Zeng
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Indoor Positioning ◽  
Lm Algorithm

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.

Kinematics Research of 6R Arc Welding Robot Based on Radial Basis Function Neural Network

2012 ◽  
Vol 163 ◽  
pp. 247-250
Author(s):  
De Zheng Song ◽  
Chao Yun
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Inverse Kinematics ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Rbf Neural Network ◽  
Simple Calculation ◽  
Weight Coefficient ◽  
Welding Robot ◽  
Six Dof

Take serial robot with six DOF for example. On the basis of analyzing the characteristics of RBF neural network, inverse kinematics calculation of arc welding robot was achieved by RBF of six-input and single output. The forward and inverse kinematics could be seen as a nonlinear mapping between the joint space and the operation space of the robot. Take the algorithm based on RBF. Acquire RBF centers by the nearest neighbor clustering algorithm. The inverse kinematics of robot was solved. Through learning the training samples of the positive solutions to determine weight coefficient of neural network, the robots pose could be accurately solved. The example shows that the algorithm has the characteristics of simple calculation and effective solution, etc. The cumbersome derivation of traditional methods is avoided. It can be seen as kinematics trajectory tracking controller of serial mechanism system.

scholarly journals Research on the Prediction Method of Stock Price Based on RBF Neural Network Optimization Algorithm

2021 ◽  
Vol 235 ◽  
pp. 03088
Author(s):  
Hongzheng Li ◽  
Shaohang Huang
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Optimization Algorithm ◽  
Financial Management ◽  
Stock Price ◽  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Future Trend ◽  
Stock Prediction

With the development of social economy, people pay more and more attention to investment and financial management. However, due to the strong volatility of the stock market, it is difficult to accurately predict the future trend of stock and the investment risk is very high. This paper proposes an optimization algorithm based on RBF neural network to predict the stock price. On the basis of RBF neural network, K-means clustering algorithm is introduced to optimize the network parameters, improve the training speed and prediction accuracy of the algorithm, and set corresponding evaluation indexes to evaluate the performance of the algorithm. The method proposed in this paper is applied to the stock prediction of stock market, and the closing price of several stocks in a period of time is predicted. The experimental results show that the method proposed in this paper has better prediction accuracy than other methods, and it is practical in the field of stock prediction.

Noise-Robust Speech Recognition Based on RBF Neural Network

2011 ◽  
Vol 217-218 ◽  
pp. 413-418
Author(s):  
Xue Mei Hou
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Recognition System ◽  
Robust Speech Recognition ◽  
Rbf Network ◽  
Noise Robust Speech Recognition ◽  
Noise Robust

Considering the actuality of current speech recognition and the characteristic of RBF neural network, a noise-robust speech recognition system based on RBF neural network is proposed with the entire-supervised algorithm. If the traditional clustering algorithm is employed, there is a flaw that the node center of hidden layer is always sensitive to the initial value, but if the entire-supervised algorithm is used, the flaw will not turn up, and the classification ability of RBF network will be enhanced. Experimental results show that, compared with the traditional clustering algorithm, the entire-supervised algorithm is of higher recognition rate in different SNRs than that of clustering algorithm.

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