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A hybrid real-valued negative selection algorithm with variable-sized detectors and the k-nearest neighbors algorithm

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KNOWLEDGE-BASED SYSTEMS 2021年 232卷 1页

作者： Li, Zhiyong Li, Tao He, Junjiang Zhu, Yongbin Wang, Yunpeng Sichuan Univ Sch Cyber Sci & Engn Chengdu 610065 Peoples R China Honghe Univ Informat Technol Ctr Mengzi 661199 Yunnan Peoples R China

A negative selection algorithm generates detectors to realize abnormality detection by simulating the maturation process of T cells in human immunity. Holes are areas of feature space that cannot be covered by the detector set and are the major factor in the degradation of algorithm performance. Conventional methods alleviate the hole problem by minimizing the coverage area of the holes. In this study, we approach the issue from a different angle. Holes are prone to form in the boundary area between the self and nonself regions, and when the self and the nonself cross or overlap, the hole problem becomes more serious. The k-nearest neighbors (k-NN) algorithm is more suitable than other methods for pending instance sets where the class domain crosses or overlaps more. Therefore, we propose a hybrid real-valued negative selection algorithm with variable-sized detectors (V-Detector) and the k-NN algorithm, abbreviated as V-Detector-kNN. The V-Detector-kNN hybrid algorithm first uses the V-Detector algorithm to classify, and then, for the problem that the nonself instances in the holes are misclassified as selfs, k-NN is introduced to classify those misclassified instances to improve the detection rate. Theoretical analysis proves that the V-Detector-kNN algorithm that we proposed has a higher detection rate than the V-Detector algorithm in most cases. Comparative experiments with 5 different algorithms on 9 UCI datasets show that our proposed algorithm ranks first in detection rate. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm V-Detector k-nearest neighbors Anomaly detection

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Optimization and Application of Real-Valued negative selection algorithm

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Procedia Engineering 2011年 23卷 241-246页

作者： Xu Aiqiang Liu Yong Zhao Xiuli Yang Chunying Li Tingjun Naval Aeronautical and Astronautical University Yantai 264001China

Concerned with the problem of lacking fault samples of complex equipments, it studies the principle and application of negative selection algorithm of artificial immune system. The detectors generation mechanism of real-valued negative selection algorithm is introduced. Intuitively, to maximize the covering produced by a set of detectors, it is necessary to reduce their overlapping and not covering the self set. This paper presents an optimization strategy base on re-heating simulated annealing algorithm to modify the position of detectors, not changing their number. This method can improve the covering effect of non-self space. The triangle training data are used to demonstrate the properties of optimized VRNS. Detection rate is improved and false alarming rate is decreased. It is used for fault detection in analog circuit; result demonstrates that the proposed algorithm is better than artificial neural network in fault detection of this circuit.

关键词： negative selection algorithm re-heating simulated annealing optimization fault detection

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A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery

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Journal of Harbin Institute of Technology(New Series) 2009年第1期16卷 5-10页

作者：窦唯刘占生 School of Energy Science and Engineering Harbin Institute of Technology

To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.

关键词： artificial immune system negative selection algorithm abnormality monitor image recognition rotating machinery

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Research on Intrusion Detection System based on Improved negative selection algorithm

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第10期7卷 494-501页

作者： Zhihao She Mengqian Li

Through the learning and research of the immune negative selection algorithm, after learning the traditional partial matching rules, namely Hamming matching and r-continuous bit matching, summarize its advantages and improve its disadvantages to create an improved matching scheme to deal with immune negatives Select the generation of the detection set in the algorithm and the process of intrusion detection. At the same time, this paper also established an intrusion detection model based on the improved scheme and a traditional intrusion detection model, and compared the matching rate and false alarm rate (the probability of black holes) between the two models through simulation experiments. It is concluded that the matching rate and false alarm rate of the improved scheme are better than those of the traditional scheme.

关键词： Intrusion Detection Artificial Immune negative selection algorithm Custom Coding

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A New negative selection algorithm and its Application in the Fault Diagnosis of the Pump-jack

A New Negative Selection Algorithm and its Application in th...

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7th World Congress on Intelligent Control and Automation

作者： Ren, Weijian Wang, Qiong Daqing Petr Inst Daqing 163318 Heilongjiang Pr Peoples R China

ISBN: (纸本)9781424421138

The fault diagnosis of the pump-jack is as the background in the paper. A new negative selection algorithm is proposed combining the advantage of genetic algorithm and simulating annealing algorithm. The initial values of the detectors are initialized by genetic algorithm, thus the diversity of the detectors is retained, the scope of detecting is enlarged. The variable radius of detectors is introduced to cover non-self space efficiently. The redundancy of detectors is reduced and the efficiency is improved by using simulating annealing. The method is used to diagnosis the faults of the pump-jack. The results are better. Especially the method can diagnosis unknown faults. It has great potentiality.

关键词： negative selection algorithm fault diagnosis simulating annealing algorithm variable radius

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Based on negative selection algorithm to generate long-period pseudo-random sequence

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Research Journal of Applied Sciences, Engineering and Technology 2012年第14期4卷 2167-2170页

作者： Yue, Mingyu Department of Electronic Information Sichuan University Chengdu 610065 China Chongqing City Management College Information Center Chongqing 400055 China

This study presents a method of generating long-period pseudo-random sequence based on a plaintext and negative selection algorithm for real applications. It is a new method that extracts the generated factor from the plaintext. Moreover, the generated factor as a group is expanded by the way of the m-sequence and generates pseudo-random. The autocorrelation and statistical properties of the method are better. If it used for data encrypted, it has a good anti-attack performance. In addition, the generate factor can also be used for immune identification in receiver end, to verify data integrity. So there will be a good prospect. © Maxwell Scientific Organization, 2012.

关键词： Generated factor Long-period negative selection algorithm Pseudo-random sequence Simulation

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An Outlier Robust negative selection algorithm

An Outlier Robust Negative Selection Algorithm

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2009 ISECS International Colloquium on Computing, Communication, Control, and Management

作者： Guiyang Li,Tao Li,Haibo Li,Jie Zeng School of Computer Science Sichuan University Chengdu,China

Traditional negative selection algorithms do not perform any differentiation for training self dataset and only use the mechanism of negative *** will generate excessive invalid detectors and have poor detection performance when the training selves contain noisy *** this paper,an outlier robust algorithm is *** new algorithm will divide the training selves into internal selves, boundary selves and outlier *** the same time,the information hiding in different kind of selves is fully utilized. Furthermore,by combining negative selection mechanism with positive selection mechanism,the new algorithm can cover the non-self region more *** experiment results show that no matter the training self data is clean or not,the new algorithm can obtain better detection performance by using fewer detectors.

关键词： negative selection algorithm Outlier self Boundary self Hypothesis testing ROC

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Application of negative selection algorithm in the Fault Detection and Diagnosis of LRE

Application of Negative Selection Algorithm in the Fault Det...

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作者： Anbo MING Wei ZHANG Ganyang TIAN Wei ZHENG The High-Tech Institute of Shaan Xi

To overcome the obstacles existed in the fault detection and diagnosis of liquid rocket engine(LRE) such as the lack of real-time, in-time, and veracity, the negative selection algorithm of artificial immune system was introduced. By constructing the artificial recognition ball(ARB) and adopting the maximum similarity rule, the fault detection and diagnosis were carried out. The experiment was performed with steady state data collected in ground test then. In the multi-dimensional modal space, the fault property was discussed further. Faults surround the normal state in different distance. If the parameters deflect more from the values which were supposed, the fault will be more obvious and it will move away from the normal state in the ARB. Some faults are separable, but some are also lapped over partly. Results show that this method has the function of quickly detection, high rate of correction diagnosis, and powerful ability of discovering the unknown fault. It can be widely applied in the state monitoring and fault diagnosis of LRE.

关键词： Liquid Rocket Engine Artificial Immune System Fault Detection and Diagnosis negative selection algorithm Artificial Recognition Ball

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Feature Interval Matching Based negative selection algorithm for Anomaly Detection

Feature Interval Matching Based Negative Selection Algorithm...

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智能计算及其应用国际会议

作者： YU Qiong1, WU Zejun1, 2, LIANG Yiwen 1 (1.School of Computer Science, Wuhan University, Wuhan 430079, China) (2.School of Information Science, Zhongnan University of Economics and Law, Wuhan 430060, China)

Inspired by human immune system, artificial immune system is widely applied to computational fields, especially to the field of anomaly detection. Antibody-antigen matching is the basis for recognitio

关键词： negative selection algorithm feature interval matching interval tag vector r-chunk

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Motor Fault Detection and Diagnosis Based on negative selection algorithm

Motor Fault Detection and Diagnosis Based on Negative Select...

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2015 5th International Conference on Information Engineering for Mechanics and Materials(ICIMM 2015)

作者： Lihua Zhou Zhongjian Dai Yaping Dai Linhui Zhao School of Automation Beijing Institute of Technology School of Mechatronics Beijing Union University

This paper presents a motor fault diagnosis method based on negative selection algorithm. It has the structure of two-level detectors, the first level detector detecting the presence of faults and the second level detector detecting the type of faults. Therefore the first level detectors are trained by using motor normal signals, and the second level detectors are trained by using several types of fault signals. During the process of detecting, only the test results of first level detectors are abnormal, the second level detectors are activated and implement fault detection to identify fault type. In this paper, normal vibration signals of motor bearing and three types of fault signals from American Case Western Reserve University bearing fault database are used to verify the fault diagnosis method. The experimental results show that the method can effectively detect early failure and can correctly identify the fault type.

关键词： artificial immune system negative selection algorithm fault detection fault diagnosis

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