On the versatility of radial basis function neural networks: A case study in the field of intrusion detection

Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of “certainty”. These properties support an analysis of radial basis function classifiers and allow for an adaption to “novel” kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.     

Mass function derivation and combination in multivariate data spaces

In this paper, we consider the problem of how to derive mass functions systematically from data samples. We also consider the ensuing problem of how to combine different mass functions derived in this way. We show that a mass function can be efficiently and systematically derived from multivariate data. We also demonstrate that combining mass functions obtained in this manner can be done easily. The way of deriving and combining mass functions is illustrated with a simple example.     

基于SVM用户建模的核函数选择研究

用户建模是从用户偏好数据中建立用户偏好模型的过程,用户偏好数据具有系统运行初期的稀疏性和非线形的特点。支持向量机(Support Vector Machine,简称SVM)具有小样本学习、非线形处理的能力,是合适的用户建模工具。SVM的非线形处理能力主要依赖于核函数,采用不同的核函数进行建模对模型的预测效果有重大影响。本文重点研究核函数的选择对基于SVM建模方法的影响,从中选取了表现较优的小波核函数,构建性能突出的SVM进行用户建模。实验证明该建模方法可以有效地从小样本数据中学习用户偏好信息,建立反映用户真实偏好的用户模型。     

一种基于小波阈值函数的交通图像去噪算法

针对高速运动条件下交通实时监测图像的失真和去噪问题,介绍小波阈值图像去噪过程和传统GCV阈值的原理,引入整数小波变换和合并递推运算的思想,提出一种快速递推GCV阈值的小波图像去噪方法。仿真实验分析了传统GCV阈值和快速递推GCV阈值去噪的效果,并比较了两种算法的复杂度。结果表明,快速递推GCV阈值算法不仅保留了传统GCV阈值算法去噪效果明显的特点,而且很好地解决了算法复杂度较高的问题,具有良好的应用前景。     

活用函数在EXCEL中实现数据库应用

Excel软件在当今社会中已经成为一种必备工具。它除了可以用来制作各种表格与图表、进行简单的运算之外,还可以管理数据,并以此为基础进行数据分析。但是大部分人在使用Excel时,只是简单使用了软件菜单提供的排序、自动筛选等常用功能,以及求和等数学计算函数,在处理最简单的数据表格时相当方便快捷,然而当实际工作对数据的处理提出更复杂的要求时就束手无策了,转而求助于他人或其他软件工具。该文将结合自己在工作中的实践,探讨通过灵活应用函数在Excel中实现数据库应用,解决复杂数据处理问题的一些方法和技巧。     

虚函数的多态性机制分析

在面向对象程序设计中,继承性映射了现实世界中事物之间的层次现象,而多态性则是映射事物自身行为的多外延现象;虚函数可以实现不同的对象在收到相同的消息时,产生不同的动作。重载是同一对象在收到相同的消息时,产生不同的动作。该文对虚函数的多态性机制进行深入分析,提出用虚函数实现多态性的四项法则。     

Roadmap for the next generation of dynamic function allocation theories and strategies

Dynamic function allocation theories have matured, but not to the extent that they can be implemented in actual environments. The authors identify a roadmap to help to get to that stage, after a brief review of relevant function allocation theories and the gaps in knowledge in adaptive allocation theories. Past research has focused on short‐term implementations of dynamic function allocation, which last for minutes (but never more than a few hours). Stable and enduring implementations of dynamic function allocation, lasting over days or possibly years, require a better and scientific understanding of the interaction among trigger mechanisms, adaptation strategies, and interruption strategies. The possibility of an unintentional consequence of this adaptive allocation, that the automated agent becomes confined to limited levels of automation, has to be ruled out after experimental evidence. © 2010 Wiley Periodicals, Inc.     

State feedback sliding mode control without chattering by constructing Hurwitz matrix for AUV movement

This paper presents a new method to eliminate the chattering of state feedback sliding mode control (SMC) law for the mobile control of an autonomous underwater vehicle (AUV) which is nonlinear and suffers from unknown disturbances system. SMC is a well-known nonlinear system control algorithm for its anti-disturbances capability, while the chattering on switch surface is one stiff question. To dissipate the well-known chattering of SMC, the switching manifold is proposed by presetting a Hurwitz matrix which is deducted from the state feedback matrix. Meanwhile, the best switching surface is achieved by use of eigenvalues of the Hurwitz matrix. The state feedback control parameters are not only applied to control the states of AUV but also connected with coefficients of switching surface. The convergence of the proposed control law is verified by Lyapunov function and the robust character is validated by the Matlab platform of one AUV model.     

多目标遗传算法在GPS动态车辆调度中的应用研究

围绕车辆调度问题多目标优化这一主题,依据企业现有的GPS定位系统,设计一种基于实数编码和精英保留策略,综合应用外部惩罚函数法的多目标遗传算法.并于其中创新性的引入基因校验算子,避免了重复车次的产生.程序使用Java语言编制,后台数据库使用oracle.实验结果表明,该算法能够充分满足系统硬约束条件,快速收敛于目标约束,完成车辆的优化调度,具有较强的实用价值.     

一种无线传感器网络目标的分布式最优覆盖算法

为了最大化目标覆盖的生命期,针对无线传感器网络的分布式自组织的特点,提出一种简单高效的分布式最优覆盖算法,引入关键目标的概念,设计基于目标覆盖能量的能量效用函数,在关键目标优先和能效优先的原则的基础上设置节点等待时间,建立节点等待时间自适应调整的机制.实验表明该算法网络生命期延长20%左右.算法复杂度低,效率高,可扩展性好.