Self-organizing behavior of a multi-robot system by a neural network approach

2004 ◽  
Author(s):  
Anmin Zhu ◽  
S.X. Yang
Keyword(s):  
Neural Network ◽  
Network Approach ◽  
Robot System ◽  
Neural Network Approach ◽  
Multi Robot ◽  
Self Organizing

A self-organizing neural network approach for automatic mesh generation

1991 ◽  
Vol 27 (5) ◽  
pp. 4201-4204 ◽  
Author(s):  
Ahn Chang-Hoi ◽  
Lee Sang-Soo ◽  
Lee Hyuek-Jae ◽  
Lee Soo-Young
Keyword(s):  
Neural Network ◽  
Mesh Generation ◽  
Network Approach ◽  
Neural Network Approach ◽  
Automatic Mesh Generation ◽  
Automatic Mesh ◽  
Self Organizing

The Influence Function of Principal Component Analysis by Self-Organizing Rule

1998 ◽  
Vol 10 (6) ◽  
pp. 1435-1444 ◽  
Author(s):  
Isao Higuchi ◽  
Shinto Eguchi
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Influence Function ◽  
Principal Component ◽  
Component Analysis ◽  
The Self ◽  
Network Approach ◽  
Neural Network Approach ◽  
Principal Component Vector ◽  
Self Organizing

This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

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