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International Conference on Machine Learning and Cybernetics

作者： Li, Gui-Yang Li, Tao Zeng, Jie Li, Hai-Bo Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China

ISBN: (纸本)9781424447053

The V-detector algorithm is a real-valued negative selection algorithm with variable-sized detectors. In this paper, several flaws existed in the algorithm are investigated and analyzed. An improved V-detector algorithm is also proposed and implemented. The improved algorithm divides the collection of self samples into boundary selves and non-boundary selves. The identifying and recording mechanism of boundary self are introduced during the generation of detectors. The experiment results showed that the new algorithm covers the holes existed in boundary between self region and non-self region more effectively than traditional negative selection algorithm does. In the meantime, the new algorithm can reduce the number of detectors under the circumstance of ensuring detection performance.

关键词： negative selection algorithm V-detector Boundary self Hypothesis testing

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The dynamic immune forecasting method of the airdrome flight delay under considering the stochastic factors

The dynamic immune forecasting method of the airdrome flight...

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9th International Conference on Hybrid Intelligent Systems (HIS 2009)

作者： Ding, Jianli Li, Xuesen Tong, Guansheng Civil Aviat Univ China Coll Comp Sci & Technol Tianjin 300300 Peoples R China

ISBN: (纸本)9780769537450

There are many uncertain stochastic factors in the process of the airdrome flight delay forecast, especially the cumulate diversification and propagation effect of the flight delays. The paper is on the base of the immune detection for the airdrome flight delay status, considering the dynamic diversification and stochastic factors of the airdrome flight delays. Propose the dynamic immune forecasting method of the airdrome flight delays under the stochastic factors and establish corresponding collection of self, detecting cells, the information of antigens and the matching model between them through setting up correspondence between the biological immune mechanism and flight operating mechanism. The result shows the method can forecast the count of flight delay in the next period of time accurately and has a good real-time characteristic.

关键词： flight delays stochastic factors negative selection algorithm dynamic forecasting

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Research on Immune Pathology in Artificial Immune System

Research on Immune Pathology in Artificial Immune System

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21st Chinese Control and Decision Conference

作者： Zhou, Xuanwu Engn Coll APF Key Lab Network & Informat Secur APF Xian 710086 Peoples R China

ISBN: (纸本)9781424427239

Artificial immune system (AIS) mimicks the superior properties of biological immune system and provides an effective method in intelligent computing and intelligent system designing. But the disease-causing mechanisms of immune pathology have led to sever security problems in artificial immune system. In the paper, we analyzed the basic principles of immune protection and immune pathology of biological system considering its application in artificial immune system. Then we take artificial immune defending system as an example to analyze the cause and potential influence of immune pathology on AIS. As to the different security problems from immunodeficiency, hypersensitivity and autoimmunity, we put forward corresponding measures to reinforce the security, robustness and stability of artificial immune system and thus effectively avoid these problems.

关键词： Immune pathology artificial immune system negative selection algorithm immunodeficiency system efficiency

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Artificial Immune system based approach to fault diagnosis for wireless sensor networks

Artificial Immune system based approach to fault diagnosis f...

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2nd International Conference on Smart Materials and Nanotechnology in Engineering

作者： Chen, Yongjun Yuan, Shenfang Wang, Yang Wu, Jian Nanjing Univ Aeronaut & Astronaut Aeronaut Key Lab Smart Mat & Struct Nanjing 210016 Jiangsu Peoples R China

ISBN: (纸本)9780819478047

Fault diagnosis has been recognized as one of the key issues in wireless sensor networks. Considering distribution feature of sensor node, however, the fault happened in wireless sensor networks is usually random and unpredictable. The conventional diagnosis approaches become increasingly difficult to deal with. As a result, the application is limited seriously. To solve the problem, a new approach based on artificial immune system for fault diagnosis is proposed. The normal and abnormal character patterns generated by a network simulator for wireless sensor networks, respectively, are regarded as the self and antigen of artificial immune system. According to a real-valued negative selection algorithm, the detectors are generated to improve the covering ability of non- self space. Taking detector as antibody, an immunity calculation is executed by the distribution zones of antibody and evolution learning mechanism of artificial immune system. The type of antigen is decided based on the clustering distribution of cloned and matured antibody. The example shows that the approach has better accuracy and the capability of self-adaptive for the fault diagnosis in wireless sensor networks.

关键词： artificial immune system wireless sensor networks negative selection algorithm fault diagnosis OPNET

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Significant Level in V-detector Generation algorithm

Significant Level in V-detector Generation Algorithm

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Asia-Pacific Conference on Information Processing (APCIP 2009)

作者： Li, Guiyang Li, Tao Zeng, Jie Li, Haibo Sichuan Univ Sch Comp Sci Chengdu 610064 Peoples R China

ISBN: (纸本)9780769536996

The V-detector generation algorithm is a kind of negative selection algorithm (NSA) inspired by biological immune system (BIS). In this paper, V-detector generation algorithm is simply introduced. The problem that V-detector generation algorithm call not meet the change of parameters in significant level is also pointed out. This problem will cause instable algorithm performance. In the meantime, it will cause the algorithm failed under the circumstance of some parameter values. This paper analyzed the reason Him caused file problem theoretically and file relevant solution is proposed. The experiment results showed that file proposed solution solved the problem existed in original algorithm successfully and make the performance more stable and reliable. The improved algorithm can really adapt to different values of significant level and obtain better detection performance.

关键词： artificial immune system negative selection algorithm V-detector hypothesis testing significant level

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negative selection BASED FUZZY FAULT DIAGNOSIS MODEL

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2009年第4期24卷 745-753页

作者： Aydin, Ilhan Karakose, Mehmet Akin, Erhan Firat Univ Bilgisayar Muhendisligi Bolumu TR-23119 Elazig Turkey

Fault diagnosis is important to ensure the continuity in the systems that the studies in this area have increased. The effectiveness of fault diagnosis methods has been enhanced by using intelligent computing techniques. In this study, a fault diagnosis method based on fuzzy logic and negative selection is proposed. In the proposed algorithm, the broken rotor bar related features are extracted using negative selection algorithm that is a component of the artificial immune system. In addition, the direction of spectrum changing obtained using the motor current signature analysis is given to fuzzy logic system and the faults are diagnosed. A new weighted affinity measurement is presented for negative selection. The broken rotor bar faults, stator and bearing friction faults occurred in induction motors can be diagnosed by using proposed method. The output of the method gives both the fault type and the severity of fault to determine the multiple faults. The performance of proposed method is verified using healthy and faulty motor data that are obtained as simulation and experimentally.

关键词： Artificial immune systems negative selection algorithm fuzzy logic induction motor fault diagnosis

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Generating an Approximately Optimal Detector Set by Evolving Random Seeds

Generating an Approximately Optimal Detector Set by Evolving...

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8th IEEE International Conference on Dependable, Autonomic and Secure Computing

作者： Zhang, Jie Luo, Wenjian Xu, Baoliang Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9780769539294

The detector generation algorithm is the core of a negative selection algorithm (NSA). In most previous work, the NSAs generate the detector set randomly, which cannot guarantee to obtain an efficient detector set. To generate an approximately optimal detector set, in this paper, a novel detector generation algorithm for the Real-Valued negative selection algorithm (RNSA) is proposed. The proposed algorithm, named as the EvoSeedRNSA, adopts a genetic algorithm to evolve the random seeds to obtain an optimized detector set. The experimental results demonstrate that the EvoSeedRNSA has a better performance.

关键词： negative selection algorithm Genetic algorithm Detector Generation algorithm Random Seed

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The dynamic immune forecasting method of the airdrome flight delay under considering the stochastic factors

The dynamic immune forecasting method of the airdrome flight...

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2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

作者： Jianli Ding Xuesen Li Guansheng Tong College of Computer Science and Technology Civil Aviation University of China Tianjin 300300 China Information Technology Research Base Civil Aviation Administration of China Tianjin 300300 China

关键词： flight delays stochastic factors negative selection algorithm dynamic forecasting

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Significant Level in V-detector Generation algorithm

Significant Level in V-detector Generation Algorithm

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2009 Asia-Pacific Conference on Information Processing

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

The V-detector generation algorithm is a kind of negative selection algorithm (NSA) inspired by biological immune system (BIS). In this paper, V-detector generation algorithm is simply introduced. The problem that V-detector generation algorithm can not meet the change of parameters in significant level is also pointed out. This problem will cause instable algorithm performance. In the meantime, it will cause the algorithm failed under the circumstance of some parameter values. This paper analyzed the reason that caused the problem theoretically and the relevant solution is proposed. The experiment results showed that the proposed solution solved the problem existed in original algorithm successfully and make the performance more stable and reliable. The improved algorithm can really adapt to different values of significant level and obtain better detection performance.

关键词： artificial immune system negative selection algorithm V-detector hypothesis testing significant level

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A Detector Generating algorithm Based on Interval Partition

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2009年第1期5卷 27-27页

作者： Xia, Huifen Ju, Shiguang Cai, Tao Jiangsu Univ Dept Comp Sci Zhenjiang 212031 Peoples R China

Based on the functions and some relevant theories of the biological immune system, an artificial immune system is established to solve the practical problems for computing systems. At present, the artificial immune system includes two major categories: the mechanism of non-self recognition and immune network, the most important of which is negative selection algorithm. The negative selection algorithm is proposed to simulate the formation and running mechanism of T cells for the immune system in 1994. In this algorithm, one of the key steps is the detector generation. Unfortunately, the current detector generating algorithms have detector generation inefficiencies, holes area, and redundant detector problems to some degree. In this paper, from the perspective of one dimension, a novel detector generating algorithm that is based on interval partition is proposed. At the beginning of this algorithm, we make the maximal interval be the initial detector;second, this detector should experience the training of self-tolerance. According to the matching rule, we let this detector match the given collection of selves;then we remove the points from the interval detector which matches the selves. At the same time, we divide the interval into two parts at this point and have the candidate detectors optimized by the corresponding interval collations and amalgamations. That is to say, the initial detector interval is divided recursively according to the spatial locations of selves. At last, we can get a set of excellent mature detectors, which can be used to protect the system security. To illustrate the advantage of this algorithm, we have given an example. From this example, we can declare that the algorithm improves the current detector generations and matching rules greatly. It also helps to remove the holes area and redundant detectors. Therefore, both the detector generation efficiency and the detecting efficiency are well improved. By the theoretical analysis and compariso

关键词： negative selection algorithm Detector generating algorithm Matching rule

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