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| 1 | A self-adaptive negative selection algorithm used for anomaly detection显示文摘A novel negative selection algorithm (NSA),which is referred to as ANSA,is presented. In many actual anomaly detection systems,the training data are just partially composed of the normal elements,and the self/nonself space often varies over time. Therefore,anom-aly detection system has to build the profile of the system based on a part of self elements and adjust itself to adapt those variables. However,previous NSAs need a large number of self elements to build the profile of the system,and lack adaptability. In order to over-come these limitations,the proposed approach uses a novel technique to adjust the self radius and evolve the nonself-covering detectors to build an appropriate profile of the system. To determine the performance of the approach,the experiments with the well-known data-set were performed. Results exhibited that our proposed approach outperforms the previous techniques. | Jinquan Zeng,Xiaojie Liu,Tao Li,Caiming Liu,Lingxi Peng,Feixian Sun Department of Computer Science,Sichuan University,Chengdu 610065,China | 2009 | Progress in Natural Science:Materials International2009，19，2： | 10 |
| 2 | A Situation-Aware Collision Avoidance Strategy for Car-Following显示文摘In this paper, we discuss how to develop an appropriate collision avoidance strategy for car-following. This strategy aims to keep a good balance between traffic safety and efficiency while also taking into consideration the unavoidable uncertainty of position/speed perception/measurement of vehicles and other drivers. Both theoretical analysis and numerical testing results are provided to show the effectiveness of the proposed strategy. | Li Li Xinyu Peng Fei-Yue Wang Dongpu Cao Lingxi Li | 2018 | IEEE/CAA Journal of Automatica Sinica2018，5，5： | 8 |
| 3 | Opposition-Based Learning Fully Informed Particle Swarm Optimizer with- out Veloeity显示文摘 | Ying Gao Lingxi Peng Fufang Li | 2013 | Swarm IntelligeneeLeeture Notes in Com- puter Science2013，，： | 1 |
| 4 | Delay-dependent robust H-infinity filtering for uncertain linear systems with time-varying interval delay显示文摘This paper proposed a design method for delay-dependent robust H-infinity filter of linear systems with uncertainty and time-varying interval delay.The proposed method was shown to be much simpler than existing ones while giving significant improvement to the existing results.The key step in the method was to construct a special type of Lyapunov functional for the filter design problem.Unlike the existing techniques,the proposed method employed neither free weighting matrices nor any model transformation,leading to reduced computational demand as well as improved performance.Numerical examples were given to demonstrate the effectiveness of the proposed method. | Peng, Chen Pei, Lingxi Yang, Jiquan | 2011 | 控制理论与应用(英文版)2011，9，2： | 0 |
| 5 | Anomaly Detection with Artificial Immune Network显示文摘Inspired by the immune network theory,an adaptive anomaly detection paradigm based on artificial immune network,referred as APAI,is proposed. The implementation of the para-digm includes:initially,the first is to create the initial antibody network;then,through the learning of each training antigen,the antibody network is evolved and updated by the optimal antibod-ies. Finally,anomaly detection process is accomplished by major-ity vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study,which is taken from the UCI machine learning database. The obtained detection accuracy of APAI was 97.7%,which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classi-fication properties,APAI possesses biological immune network properties such as clonal selection,immune network,and immune memory,which can be applied to pattern recognition,classifica-tion,and etc. | PENG Lingxi LI Tao LIU Xiaojie CHEN Yuefeng LIU Caiming LIU Sunjun | 2007 | Wuhan University Journal of Natural Sciences2007，12，5： | 0 |
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