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.

An Image Restoration Algorithm Based on Improved RBF Neural Network

2012 ◽  
Vol 430-432 ◽  
pp. 1671-1676
Author(s):  
Xue Zhang Zhao ◽  
Zhi Yuan Liu ◽  
Yun Jiang Xi
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Rbf Neural Network ◽  
Step Function ◽  
Network Size ◽  
Series Data ◽  
Gradient Descent Algorithm

In order to improve large amount of computing and slowly convergence speed, an improved radial basis function (RBF) neural network is raised in this paper. According to feature that the more recent data should be the more important in time-series data, it converts width value from original constant value to step function and accelerates the iterative convergence by using nearest neighbor clustering algorithm only at center, training weight by using gradient descent algorithm to correct network parameters and deleting input neurons adaptively. Network size is streamlined through network optimization training. Simulation shows that the restored image is good in visual and quantitative with faster image restoration processing. The algorithm based on improved RBF neural network has significantly improved the image restoration compared to other methods, but also well keeps image detail.

Researches on Software Cost Combined Estimation Based on RBF Neural Network and RVM

2014 ◽  
Vol 610 ◽  
pp. 325-331 ◽  
Author(s):  
Tong Zhang ◽  
Min Fang Zhang ◽  
Hua Zhang ◽  
Yu Qing Hu
Keyword(s):  
Neural Network ◽  
Evaluation Method ◽  
Rbf Neural Network ◽  
Weight Coefficient ◽  
Estimation Model ◽  
Software Cost ◽  
Advantages And Disadvantages ◽  
Vector Machines ◽  
Combined Estimation ◽  
Historical Sample

Under the precondition of relatively adequate historical sample data of obtainable software cost, the thesis makes comprehensive analysis of the advantages and disadvantages of complementary neural network and vector machines, and attempts to study the software cost combined estimation based on RBF neural network and RVM and to build combined estimation model, then applies the entropy evaluation method to identify the weight coefficient of this combined estimation model, and finally it adopts the data from COCOMO database to verify this combined estimation as well as the rationality and scientificity of this model.

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