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.
29
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.
20
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
Immune network simulations in multicriterion design
J. Yoo,Prabhat Hajela +1 more
TL;DR: A modification to the genetic algorithm (GA) based search procedure, based on the modeling of a biological immune system, is proposed as an approach to solving the multicriterion design problem.
145
A study of artificial immune systems applied to anomaly detection
Fabio A. González,Dipankar Dasgupta +1 more
- 01 Jan 2003
TL;DR: The experimental results show that the proposed representations along with the proposed algorithms provide some advantages over the binary negative selection algorithm, including improved scalability, more expressiveness that allows the extraction of high-level domain knowledge, non-crisp distinction between normal and abnormal, and better performance in anomaly detection.
97
Artificial immune optimization methods and applications - a survey
Xiaolei Wang,Xiao-Zhi Gao,Seppo J. Ovaska +2 more
- 10 Oct 2004
TL;DR: This paper gives a concise survey on the recent progresses of the theory as well as applications of the AIO schemes, in which some representative approaches are briefly introduced and discussed.
90
A hybrid optimization algorithm in power filter design
Xiaolei Wang,Xiao-Zhi Gao,Seppo J. Ovaska +2 more
- 01 Jan 2005
TL;DR: A hybrid optimization algorithm based on the principles of the CSA and MEC to search for the optimal parameters of a passive filter in the diode full-bridge rectifier of a power filter design.
29
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.
29