基于人工免疫原理的入侵检测模型研究

本文在研究入侵检测技术和免疫学原理的基础上,分析了免疫系统的工作机理,将其和入侵检测系统的工作原理进行了详细比较,并在研究当前各种人工免疫算法的基础上,建立了一个新的基于人工免疫的入侵检测模型框架．实现了当前智能化入侵检测系统所要求的自学习、自适应、分布式和可扩展功能,具有一定的现实意义。     

基于人工免疫原理的入侵检测模型研究

本文在研究入侵检测技术和免疫学原理的基础上,分析了免疫系统的工作机理,将其和入侵检测系统的工作原理进行了详细比较,并在研究当前各种人工免疫算法的基础上,建立了一个新的基于人工免疫的入侵检测模型框架,实现了当前智能化入侵检测系统所要求的自学习、自适应、分布式和可扩展功能,具有一定的现实意义。     

免疫优化神经网络对倒立摆的控制

人工免疫算法是基于生物免疫系统机理和特点发展而来的一种新型智能算法。针对BP神经网络传统训练算法的学习效率低、收敛速度慢且容易陷入局部最优等不足,采用免疫算法对BP神经网络权值进行优化,以实现对倒立摆的控制。实验结果表明,用免疫算法训练神经网络收敛速度快,并能有效避免陷入局部最优。优化后的神经网络能对实际的倒立摆装置进行控制,并取得了满意的效果。     

Research on immune algorithm for mobile phone viruses detection

In responds to the problem that mobile phone viruses is much more serious but the detection technology for them is not maturity, there is a new way for mobile phone viruses detection. Which is based on the viruses＇ work principle and infection ways, introduced immune algorithm into computer viruses defense detection and set up mobile phone viruses detection model based on minus select immune algorithm. At the same time, it also made analysis on immune algorithm viruses detection and found an approach to probing to mobile phone viruses.     

一种基于用户行为的Self集构造和演化方法

对计算机安全免疫系统中Self集构造的可能性和特征码选取的原则进行了探讨性的分析,提出了一种基于用户行为的Self集构造方法。Self集中的特征码包含了用户的操作、操作对象和操作参数。然后阐述了一种通地用户友好度和可信度进行Self集演化的方法。     

基于免疫代理的网络故障诊断研究

针对现有网络故障诊断系统的自适应能力弱、诊断速度慢和故障模式不同等问题,将免疫原理与代理技术相结合,采用分层多步的诊断思想构建诊断模型。基于克隆选择学说,提出新算法,完成检测器的训练。该算法选取了检测器克隆群,引进优化参数,可避免过早收敛和局部最优解的产生,从而改进诊断性能;同时还引入了检测器分类思想,加速诊断过程。与传统故障诊断方法相对比,该模型在处理复杂环境下的网络故障具有较明显的优势。     

抗大肠杆菌荧光抗体的制备及其特性

活体大肠杆菌与弗氏不完全佐剂乳化后注射到新西兰大白兔体内15d后,利用两步沉淀法提取并纯化得到大肠杆菌表面蛋白抗体IgG,与FITC的交联率F/P达到2.7,未发现FITC对大肠杆菌表面蛋白抗体的特异性有显著的影响。将抗大肠杆菌荧光抗体注入小白鼠体内后,除肠道内有荧光显示外,肝脏、心脏、肾脏中未发现荧光显示,表明大肠杆菌只在小白鼠大肠存在,根据观测到的荧光强度可定性和定量地判断大肠中的大肠杆菌。     

A parallelized artificial immune network for fuzzy clustering

An artificial immune network (AIN), AINFCM, has been successfully used for fuzzy clustering to overcome the shortage of FCM algorithm that is sensitive to the selection of initial centres. However, as a stochastic searching algorithm, the runtime of AINFCM goes up especially when dealing with large quantities of data or generating much more antibodies for clone selection. In this paper, the PAINFCM is proposed for parallel affinity calculation of antibodies according to time complexity of AINFCM algorithm. Subsequently, a coarse-grained version of PAINFCM algorithm was proposed to parallelize clone expansion. Experiments indicated that the PAINFCM improves efficiency of AIN. Furthermore, it provides a good balance between global searching ability and run time of the AIN.     

基于免疫算法的物流配送中心选址

针对物流配送中心选址问题,以物流成本为目标函数,采用免疫算法对配送中心选址进行优化.通过一个中等规模的物流需求点实例,仿真结果表明,该方法能够快速有效地求得物流配送中心选址问题的全局最优解.     

Self-adaptive learning based immune algorithm

A self-adaptive learning based immune algorithm (SALIA) is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned problems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms (CLONALG), and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10⁷ in average.