The fault detection method based on the negative selection algorithm is analyzed in-depth.
系统地研究了基于反面选择算法的故障检测方法。
The use of negative selection gene (HSV-tk) results in 7-fold increase at selection efficiency.
负向选择系统的应用使同源重组事件的富集效率提高了7倍。
The negative selection algorithm for an artificial immune system was used to detect vibration signals.
该文对人工免疫系统中的负向选择算法进行探讨。
A matching algorithm based on the negative selection for anomaly detection was presented in this paper.
使用了一种改进的否定选择匹配算法来检测异常行为。
In chapter 4, negative selection mechanics in immune system and negative selection algorithm are studied.
第四章分析了免疫系统阴性选择机理及已有的阴性选择算法。
An advanced negative selection algorithm adapted to the engineer needs in vehicle online testing is presented.
结合车辆在线检测背景，提出了一种改进型阴性选择算法。
The algorithm mainly utilized clonal selection and negative selection principles to learn the characteristics of antigens.
该方法主要利用克隆选择和阴性选择原理来学习抗原的特性，实现对抗体的促进和抑制。
How to effectively generate detectors is one of the important problems of negative selection algorithm and its practicability.
检测器生成是非选择算法的关键步骤。
The experimental results showed that the precision was high in detecting abnormal flight data based on improved negative selection algorithm.
实验表明，采用改进编码后的阴性选择算法识别飞行数据中的异常值具有较高的精度。
The MHC restriction of t cell is determined in positive selection. The tolerance of t cells to autoantigen is acquired during negative selection.
阳性选择决定了成熟T细胞的MHC限制性，阴性选择使机体获得自身免疫耐受。
Because thymic dendritic cells are the potent antigen presenting cells in the thymus, they must play an important role in thymic negative selection.
胸腺树突状细胞作为胸腺内功能强大的抗原呈递细胞，在阴性选择中必定发挥着不可忽视的作用。
This paper analyses mechanisms needed for a negative selection algorithm in an artificial immune system and defines fuzzy similarity using fuzzy logic.
利用模糊思想，在定义了模糊相似度与背离度的基础上，提出了一种生成最有效检测器集的变阈值免疫阴性选择算法。
The approach is based on negative selection mechanism of the natural immune system and combined with artificial neural network to be used for monitoring.
该方法是基于生物免疫系统反面选择机理，并结合人工神经网络进行监测的一种方法。
This approach is based on negative selection mechanism of the natural immune system and combined with artificial neural network to be used for monitoring.
该方法是基于生物免疫系统反面选择机理，并结合人工神经网络进行监测的一种方法。
The intrusion detection system employs negative selection algorithm to create detectors, optimizes them continuously and maintains efficient memory detectors.
它使用负筛选算法来生成检测规则，并不断地进行检测规则优化以剔除冗余的检测规则，保留高效的检测规则。
This paper proposes two modified negative selection algorithm through the reference of fuzzy mathematics, applied to different intrusion detection environment.
本文借鉴模糊数学思想提出两种改进的否定选择算法，分别适用于不同的入侵检测环境。
In order to solve these problems, an intrusion detection system model based on AIS is put forward. Detection rules are produced by negative selection algorithm.
为了解决这些问题，提出了一个基于人工免疫原理的入侵检测模型，该模型使用负筛选算法产生检测规则集。
For the anomaly detection in the vibration time series of the rotor system, a real-valued negative selection algorithm based on Euclidean distance has been implemented.
针对转子振动时间序列中异常数据的检测问题，采用欧氏距离进行匹配计算，在实数域实现了负向选择算法。
The system is made up of four modules, such as gene library evolution, clone selection, negative selection, immune memory module. The four patterns form an organic integer.
该系统由四个紧密联系的模块组成：基因库进化、克隆选择、否定选择和免疫记忆，四者形成一个有机的整体。
In the artificial immune based intrusion detection systems, mature detectors complete the tolerance and generation process mainly by negative selection algorithm currently.
目前在基于人工免疫的入侵检测系统中，成熟检测器的耐受和生成过程主要采用否定选择算法实现。
To increase antibody generating speed in artificial immune system, a chaos negative selection algorithm was presented which is based on the chaotic character of immune system.
为了提高人工免疫系统中抗体生成速度，基于免疫系统的混沌特征，提出了混沌否定选择算法。
The shortcoming of binary negative selection algorithm and the excellence of real value coding based on the negative selection mechanism of artificial immune systems is analysed.
在人工免疫系统反向选择机理的基础上，分析了二进制反向选择算法的缺点及采用实值编码的优点。
The shortcoming of binary negative selection algorithm and the excellence of real value coding based on the negative selection mechanism of artificial immune systems is analysed.
基于人工免疫系统带变异的否定选择算法的思想，对滑坡监测数据进行处理，根据部分匹配原则产生检测器，寻找滑坡突变点。
But there are still some problems on real-valued negative selection algorithm, such as needing large amounts of time in generating detectors and setting the position of detectors.
但是实值负选择算法仍然存在着一些问题，比如：产生检测器和设置检测器位置需要消耗大量的时间。
The immunology principles on which Artificial immune System depends mainly include immune network theory, the principle of clonal selection and the principle of negative selection.
人工免疫系统所依据的免疫学原理主要包括免疫网络理论，克隆选择和阴性选择原理。
Based on exhaustive algorithm, a new algorithm is presented as detectors generating method in the negative selection model, including its design, performance analysis and experiment.
研究的重点是负选择模型中初始检测器集的生成算法，在穷举法的基础上提出了一个新的检测器生成算法，包括算法的设计、性能分析和试验。
The matching affinity between two vectors was measured using cosine similarity to develop a real-valued negative selection algorithm with the matching calculation in the real domain.
通过沿时间轴对采样信号加窗的方式构造向量集合，利用余弦相似度进行向量间亲合度的匹配计算，实现在实数域进行匹配计算的实数值负向选择算法。
To improve the performances of detectors, the real-valued negative selection algorithm was proposed by improving detector generation algorithm based on the negative selection algorithm.
为了提高检测器的性能，在负选择的基础上改进检测器生成算法而产生了实值负选择算法。
In order to detect the main faults well and truly, a new approach of fault detection for gas valves was proposed based on negative selection mechanism of immune system and neural network.
为了能够准确地检测出气阀的主要故障，从免疫系统反面选择机理出发，结合神经网络，提出了基于免疫神经网络的活塞压缩机气阀故障检测的方法。
In order to detect the main faults well and truly, a new approach of fault detection for gas valves was proposed based on negative selection mechanism of immune system and neural network.
为了能够准确地检测出气阀的主要故障，从免疫系统反面选择机理出发，结合神经网络，提出了基于免疫神经网络的活塞压缩机气阀故障检测的方法。
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