基于疫苗理论的变阈值免疫阴性选择算法

为了提高变阈值免疫阴性选择算法的检测效率,对算法的监测系统异常部分进行了改进设计,建立并实现了算法的"适应性免疫反应"功能.在算法中加入了疫苗算子和阳性选择算子,建立了具有动态刷新功能的抗体库.抗体库在动态刷新的过程中去掉了冗余,使冗余的消除也具有自适应性.仿真结果表明,变阈值免疫阴性选择算法经改进设计后,具有较强的自...     

自适应熵疫苗算子的免疫图像分割方法

本文针对二维熵图像分割算法存在计算量巨大的问题,基于免疫调节机制,设计了一种新的图像分割方法．该方法利用免疫疫苗理论,并与熵理论相结合,提出了熵疫苗算子．熵疫苗作为抗体疫苗进行选择,通过熵运算,提取更新疫苗库,从而使得最优分割阈值的形成及疫苗库的更新具有自适应性,疫苗的选择更加准确有效．仿真实验表明熵疫苗算子加快了算法的搜索速度、提高了算法的适应性．该算法对复杂图像具有良好分割效果和较强的实时处理能力．     

一种基于阴性选择的免疫检测器生成算法

通过简单介绍计算机免疫系统的原理,并对传统阴性选择算法进行分析,针对其不足,提出了一种新的生成检测器的改进算法。对匹配规则的阈值可变,并引入了全局匹配和局部匹配的概念,提高了生成的检测器成为成熟检测器的概率。同传统的阴性选择算法相比,大大减少了不可避免的“黑洞”数量,提高了检测率。     

直觉模糊逻辑非算子与蕴涵算子的一些性质及证明

给出了直觉模糊逻辑非算子的定义,讨论了非算子的一些性质。提出了关于直觉蕴涵算子的两个重要定理,并给出了证明     

一种基于模糊思想的变阈值免疫阴性选择算法

通过对人工免疫系统中阴性选择算法机理的分析,利用模糊思想,定义了模糊相似度,并在此基础上,提出了一种基于模糊思想的变阂值免疫阴性选择算法．该算法匹配阂值可变,采用调整匹配阈值的方法大幅降低黑洞数量;在满足一定模糊相似度的前提下,实现了带控制参数的模糊匹配,模糊程度可控．仿真结果表明,该算法生成的成熟检测器检测范围较大,空间覆盖率高,黑洞数量大幅下降,同时检测率有显著提高,算法具有较强的鲁棒性。     

实现多视角匹配的一种分块模糊投影决策算法

为实现多摄像头监控系统中同一时刻各视角图像的多目标匹配,提出了以目标分块子模块为基本单位,模糊投影至多层空间平面,利用投票决策实现匹配候选项判优的方法.该算法在提高匹配准确性的同时,不仅极大程度地修正了检测误差的影响,且相比传统的投票匹配算法,显著降低了计算复杂度.算法经公开测试视频数据库验证,具有高效性和鲁棒性.     

模糊蕴涵算子的相等问题

在文献[1]中,S.Weber介绍了三类模糊蕴涵算子,据此,本文讨论了其前两种类型的模糊蕴涵算子及S-蕴涵算子三者之间的相等问题,并利用强DeMorgan三元组的性质,给出了两两相等的充要条件.     

混合匹配规则的否定选择算法

针对目前基于免疫的检测器生成算法中,匹配规则单一造成的检测器质量低下的问题,提出一种基于混合匹配规则的否定选择算法,采用海明距离整体匹配的各段内R连续比特匹配的否定选择算法,即混合匹配否定选择算法,对算法的性能进行了分析．实验结果表明,该算法能够高效地产生所需要的检测器,提高了检测率,且降低了误报率．     

模糊诱导有序加权平均算子的散度

在复杂系统中,模糊诱导有序加权平均算子是一种重要的聚合多源信息工具。借鉴信息熵理论,分析研究了模糊诱导有序加权平均算子的偏好散度和分布散度及它们的相关性质。从实际应用的角度,给出了基于偏好散度和分布散度的选择模糊诱导有序加权平均算子权重向量的优化模型。     

基于模糊β-覆盖的(I,T)-模糊粗糙近似算子

利用模糊β-邻域和模糊逻辑算子,提出两种基于模糊β-覆盖的(I, T)-模糊粗糙集模型;研究了模型中上、下近似算子的基本性质;对比分析这两对广义模糊粗糙近似算子与其他模糊粗糙近似算子之间的关系,刻画了相关近似算子等价的条件。     

A negative selection algorithm with neighborhood representation

This paper proposes a negative selection with neighborhood representation named as neighborhood negative selection algorithm.This algorithm employs a new representation method which uses the fully adjacent but mutually disjoint neighborhoods to present the self samples and detectors.After normalizing the normal samples into neighborhood shape space,the algorithm uses a special matching rule similar as Hamming distance to train mature detectors at the training stage and detect anomaly at the detection stage.The neighborhood negative selection algorithm is tested using KDD CUP 1999 dataset.Experimental results show that the algorithm can prevent the negative effect of the dimension of shape space,and provide a more accuracy and stable detection performance.     

Research on a randomized real-valued negative selection algorithm

The negative selection algorithm (NSA) is one ofthe most widely used techniques in the field of artificialimmune systems. It is primarily used to detect changesin behavior patterns by generating detectors in the com-plementary space (given normal samples). In the orig-inal version of the NSA[1], the detectors are used di-rectly to classify newdata as self (normal) or non-self(abnormal). Subsequent works have shown the feasi-bility of combining the NSA with classification algo-rithms[2]; in…     

Limitations of quantitative operator fuzzy logic

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A self region based real-valued negative selection algorithm

Point-wise negative selection algorithms, which generate their detector sets based on point of self data, have lower training efficiency and detection rate. To solve this problem, a self region based real-valued negative selection algorithm is presented. In this new approach, the continuous self region is defined by the collection of self data, the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate, and variable detectors in the self region are deployed. The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set. Results show that, more information can be provided when the training self points are used together as a whole, and compared with the point-wise negative selection algorithm, the new approach can improve the training efficiency of system and the detection rate significantly.     

Dialectic operator fuzzy logic

Dialectic operator fuzzy logic (DOFL) is presented which is relevant,paraconsistent and nonmonotonic.DOFL can vividly describe the belief revision in the cognitive process and can infer reasonably well while the knowledge is inconsistent,imprecise or incomplete.     

Image matching navigation based on fuzzy information

0　INTRODUCTIONImagematchingalgorithmsplayaveryimportantroleinimagenavigationsystems .Becauseoftheinfluenceofimagingconditionsandimagingdevices ,therearealwaysexistingintensivechangesandgeometricdistortionsbe tweenthereferenceimageandthereal timeimage .Thoughsomemethodscanbeappliedtoimprovethequali tyoftheimagebeforefurtherprocessing ,somecolorandgeometricdistortionmaystillexist,aswellasthenoisedisturbance .Forthesakeofnavigationaccuracy ,itisex pectedthatmatchingalgorithmsshouldbenotonly…     

基于模糊算法的苹果无损分拣技术研究

主要对苹果分拣技术进行了研究，并给出了苹果分拣系统的结构。由于苹果的好坏主要由苹果的大小、形状和成熟度决定；该分拣系统有三个输入参数即大小、形状和成熟度，其三个特征参数的提取应用了数字图像处理技术；一个输出变量即苹果等级分类，分别标为A、B、C三类。以三个特征参数为输入，根据模糊控制算法，最终得到苹果的等级分类。实验结果表明：该方法能较好地区分苹果的好坏，达到预期的目标，结果是可以接受的。