Proceedings Article10.1109/ICSMC.2006.385120
Clonal Optimization of Negative Selection Algorithm with Applications in Motor Fault Detection
Xiao-Zhi Gao,Seppo J. Ovaska,Xiaolei Wang,Mo-Yuen Chow +3 more
- 01 Oct 2006
- Vol. 6, pp 5118-5123
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TL;DR: Taking advantage of the clonal optimization strategy, the NSA detectors can be optimized for anomaly detection and a new motor fault detection scheme using the authors' NSA is discussed.
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Abstract: In this paper, we employ the clonal optimization method to optimize the detectors in the negative selection algorithm (NSA). Taking advantage of the clonal optimization strategy, the NSA detectors can be optimized for anomaly detection. A new motor fault detection scheme using our NSA is also discussed. We demonstrate the efficiency of the proposed approach with an example of bearings fault detection.
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Citations
A neural networks-based negative selection algorithm in fault diagnosis
TL;DR: A novel neural networks-based negative selection algorithm based on neural networks training has the distinguishing capability of adaptation, which is well suited for handling dynamical problems.
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Multi-Level Optimization Of Negative Selection Algorithm Detectors With Application In Motor Fault Detection
TL;DR: A novel amulti-level optimization strategy for the Negative Selection Algorithm detectors is proposed, based on both the Genetic Algorithms (GA) and clonal selection principle, to achieve the best fault detection performance.
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Negative Selection Algorithm Research and Applications in the last decade: A Review
Kishor Datta Gupta,Dipankar Dasgupta +1 more
- 01 Jan 2021
TL;DR: The Negative Selection Algorithm (NSA) is one of the important methods in the field of Immunological Computation (or Artificial Immune Systems). Over the years, some progress was made which turned this algorithm ( NSA) into an efficient approach to solve problems in different domain this article.
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Particle Swarm Optimization of detectors in Negative Selection Algorithm
Xiao-Zhi Gao,Seppo J. Ovaska,Xiaolei Wang +2 more
- 01 Oct 2007
TL;DR: A particle swarm optimization (PSO)-based detector optimization scheme in the negative selection algorithm (NSA), a natural immune response inspired pattern discrimination method, that is optimized by the PSO to collectively occupy the maximal coverage of the nonself space.
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References
Learning and optimization using the clonal selection principle
L. N. D. Castro,Fernando J. Von Zuben +1 more
TL;DR: This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response and derives two versions of the algorithm, derived primarily to perform machine learning and pattern recognition tasks.
•Posted Content
Self-Nonself Discrimination in a Computer
TL;DR: In this article, a method for change detection which is based on the gereration of T cells in the immune system is described. But this method is not suitable for the problem of computer virus detection.
A Randomized Real-Valued Negative Selection Algorithm
Fabio A. González,Fabio A. González,Dipankar Dasgupta,Luis Fernando Niño +3 more
- 01 Sep 2003
TL;DR: This paper presents a real-valued negative selection algorithm with good mathematical foundation that solves some of the drawbacks of the previous approach and can produce a good estimate of the optimal number of detectors needed to cover the non-self space.
•Book
Artificial Immune Systems: A New Computational Intelligence Approach
Leandro Nunes de Castro,Jonathan Timmis +1 more
- 23 Sep 2002
TL;DR: The AIS in Context with Other Computational Intelligence Paradigms and Case Studies shows how the immune system in context with other biological systems and other paradigms has changed since the 1970s.