GF-NSA: A Negative Selection Algorithm Based on Self Grid File

2010 ◽  
Vol 44-47 ◽  
pp. 3200-3203 ◽  
Author(s):  
Tao Yang ◽  
Hong Li Deng ◽  
Wen Chen ◽  
Zhe Wang
Keyword(s):  
Negative Selection ◽  
Selection Process ◽  
The Self ◽  
Selection Algorithm ◽  
Selection Efficiency ◽  
Data Set ◽  
Creation Process ◽  
Negative Selection Algorithm ◽  
Data Query

The efficiency of traditional negative selection algorithm is too low to be applied. In our algorithm before the detector creation process, the structure of self data set is pre-treated to be a grid file, and the negative selection process is transformed into a data query procedure in the self grid file to improve the negative selection efficiency. Furthermore, each detector recorded its grid id to dynamically update itself along with the variations of self data in the grid.

A Negative Selection Algorithm Base on the Self R-Tree

2013 ◽  
Vol 411-414 ◽  
pp. 2007-2012
Author(s):  
Kun Peng Wang
Keyword(s):  
Time Complexity ◽  
Negative Selection ◽  
Selection Process ◽  
The Self ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Data Query ◽  
Query Process ◽  
Algorithm Base ◽  
Selection Algorithms

In this article, we present a new negative selection algorithm which the self-data is organized as a R-Tree structure. And the negative selection process could be transformed into the data query process in the self-R-Tree, if a new detector is indexed in any leaf node it will be dropped. As the time complexity of data query process in the tree is in the log level, the negative selection process of our algorithm is superior to the linearly comparation procedure in the traditional negative selection algorithms.

A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set

2010 ◽  
Vol 121-122 ◽  
pp. 486-489
Author(s):  
Wen Chen ◽  
Tao Li ◽  
Xiao Jie Liu ◽  
Yuan Quan Shi
Keyword(s):  
Negative Selection ◽  
The Self ◽  
Hierarchical Level ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Cluster Radius ◽  
Negative Selection Algorithm ◽  
Value Range ◽  
Level Cluster

In this article, we proposed a negative selection algorithm which based on hierarchical level cluster of self dataset CB-RNSA. First the self data set is clustered by different cluster radius, and then the self data are substituted by cluster centers to compare with candidate detectors to reduce the number of distance counting. In the detector creating process, the value of each detector property was restricted to a given value range so as to decrease the redundancy of detectors. The stimulation result shows that CB-RNSA is an effective algorithm for the creation of artificial immune detectors.

NUMERICAL NEGATIVE SELECTION ALGORITHM

2010 ◽  
Vol 19 (05) ◽  
pp. 703-712
Author(s):  
TAO CAI ◽  
SHIGUANG JU ◽  
DEJIAO NIU
Keyword(s):  
Negative Selection ◽  
Detection System ◽  
Immune Algorithm ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Time And Space ◽  
Data Set ◽  
Matching Rule ◽  
Artificial Immune Algorithm ◽  
Negative Selection Algorithm

The artificial immune algorithm is the hot topic in much research such as the intrusion detection system, the information retrieval system and the data mining system. The negative selection algorithm is the typical artificial immune algorithm. A common representation of binary strings for antibody (detector) and antigen have been associated with inefficiencies when generating detector and inspecting antigen. We use a single integer to represent the detector and provide the basis of improving negative selection algorithm efficiency. In the detector generation algorithm, extracting sub-strings in self that its length is larger than threshold and converting them to single integer in numerical interval, then the rest integers in numerical interval are selected as numerical detectors. It can reduce the time and space overhead of detector generation and provide the facility to analyze the positive and negative errors when antigen inspection. The numerical matching rule is given. The B-tree is used to create index of numerical detector. Extracting sub-strings in antigen that its length is larger than threshold and converting them to some integers. If there is the same value as those integers in the index of numerical detector, then the antigen matches one numerical detector. It can improve the efficiency of antigen inspection. Finally the prototype of the numerical negative selection algorithm and negative selection algorithm are realized to test the overhead of the detector generation and antigen inspection using the live data set. The results show that the numerical negative selection algorithm can reduce the time and space overhead and avoid fluctuation of the overhead.

scholarly journals MONITORAMENTO DE FALHAS EM SISTEMAS DINÂMICOS UTILIZANDO UM MÉTODO DE ANÁLISE DE DADOS BASEADO NO ALGORITMO DE SELEÇÃO NEGATIVA

2021 ◽  
Vol 13 (2) ◽  
pp. 78-87
Author(s):  
Driely Candido Santos ◽  
Mara Lúcia Martins Lopes ◽  
Fábio Roberto Chavarette ◽  
Bruno Ferreira Rossanês
Keyword(s):  
Negative Selection ◽  
Intelligent System ◽  
Artificial Immune Systems ◽  
Base Line ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Vibration Signals ◽  
Immune Systems ◽  
Negative Selection Algorithm

This work presents the application of a method for monitoring and diagnosing failures in mechanical structures based on the theory of vibration signals and on Artificial Immune Systems to assist in data processing. It uses the Negative Selection Algorithm as a tool to identify fault samples extracted from the laboratory simulated signals of a dynamic rotor. This methodology can help mechanical structure maintenance professionals, facilitating decision-making. The data set used in the processing of the intelligent system was generated through experiments. For normal (base-line) conditions, the signals of the rotor in free operation were used, that is, without the addition of unbalance mass, and for the fault conditions, unbalance masses were added to the system. The results are satisfactory, showing precision and robustness.

Sign in / Sign up

Export Citation Format

Share Document