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.
15
References
•Journal Article
A hybrid optimization algorithm based on ant colony and immune principles
TL;DR: Simulation results demonstrate the remarkable advantage of the hybrid optimization method based on the ant colony and clonal selection principles in diverse optimal solutions, closely tracking varying optimum, as well as improved convergence speed.
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.
15
•Book Chapter
Negative Selection: How to Generate Detectors
Modupe Ayara,Jon Timmis,Rogério de Lemos,Leandro Nunes de Castro,Ross Duncan +4 more
- 01 Sep 2002
TL;DR: An on-going investigation into the usefulness of the negative selection metaphor for immune inspired fault tolerance reveals that trade-offs have to be made in the choice of algorithm based on the time and space complexities, as well as the detection rate.
Machine invention of quantum computing circuits by means of genetic programming
Lee Spector,Jon Klein +1 more
TL;DR: Using the PushGP genetic programming system and the QGAME quantum computer simulator, the invention of a new, better than classical quantum circuit for the two-oracle AND/OR problem is demonstrated.