基于协同进化算法的网络控制系统性能与安全性最优折中技术研究

传统的安全机制为了保障系统的安全性可能会牺牲网络的性能,因其系统资源的有限性.主要研究通过协同进化算法使得网络控制系统的性能和安全性达到最优折中.以DC运动系统为例,给出了性能和安全性的折中模型.同时还给出了基于协同进化算法的性能和安全性折中的最优化算法.实验结果证明协同进化算法能够非常有效地找到网络控制系统折中模型的Nash平衡.同时给出的模型还非常适用于网络控制系统性能和安全性折中的分析和最优化.     

A cooperative coevolutionary algorithm for design of platform-based mass customized products

As a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution.     

基于覆盖的多标记学习方法研究

多标记学习是实际应用中的一类常见问题,覆盖算法在单标记学习中表现出了优秀的性能,但无法处理多标记情况。将覆盖算法推广到多标记学习中,针对多标记学习的特点和评价指标,对算法的学习和构造过程进行了改造,给出待分类样本对各类别的隶属度。将算法应用于基因数据集和自然场景数据集的学习中,实验结果表明算法能够取得较好的分类效果,且相比于大多数同类算法有更高的性能。     

A hybrid coevolutionary algorithm for designing fuzzy classifiers

Rule learning is one of the most common tasks in knowledge discovery. In this paper, we investigate the induction of fuzzy classification rules for data mining purposes, and propose a hybrid genetic algorithm for learning approximate fuzzy rules. A novel niching method is employed to promote coevolution within the population, which enables the algorithm to discover multiple rules by means of a coevolutionary scheme in a single run. In order to improve the quality of the learned rules, a local search method was devised to perform fine-tuning on the offspring generated by genetic operators in each generation. After the GA terminates, a fuzzy classifier is built by extracting a rule set from the final population. The proposed algorithm was tested on datasets from the UCI repository, and the experimental results verify its validity in learning rule sets and comparative advantage over conventional methods.     

基于LVQ神经网络的改进覆盖算法

覆盖算法是一种具有高分类准确度和强泛化能力的构造性神经网络分类算法。针对其选择覆盖中心的随意性,结合竞争性神经网络方法对覆盖算法进行改进,在覆盖学习之前进行预学习,选择最佳覆盖球形中心,来优化覆盖。通过标准UCI测试数据实验的比较,从分类的准确性和覆盖个数方面进行对比,得到改进的覆盖算法有很好的效果。     

复杂模糊分类系统的协同进化设计方法

提出一种基于协同进化算法的复杂模糊分类系统的设计方法．该方法由以下3步组成:1)利用Simba算法进行特征变量选择;2)采用模糊聚类算法辨识初始的模糊模型;3)利用协同进化算法对所获得的初始模糊模型进行结构和参数的优化．协同进化算法由三类种群组成;规则数种群,规则前件种群和隶属函数种群;其适应度函数同时考虑模型的精确性和解释性,采用三类种群合作计算的策略．利用该方法对多个典型问题进行分类,仿真结果验证了方法的有效性．     

Rule learning for classification based on neighborhood covering reduction

Rough set theory has been extensively discussed in the domain of machine learning and data mining. Pawlak’s rough set theory offers a formal theoretical framework for attribute reduction and rule learning from nominal data. However, this model is not applicable to numerical data, which widely exist in real-world applications. In this work, we extend this framework to numerical feature spaces by replacing partition of universe with neighborhood covering and derive a neighborhood covering reduction based approach to extracting rules from numerical data. We first analyze the definition of covering reduction and point out its advantages and disadvantages. Then we introduce the definition of relative covering reduction and develop an algorithm to compute it. Given a feature space, we compute the neighborhood of each sample and form a neighborhood covering of the universe, and then employ the algorithm of relative covering reduction to the neighborhood covering, thus derive a minimal covering rule set. Some numerical experiments are presented to show the effectiveness of the proposed technique.     

基于混合策略的关联分类方法

关联分类中现有的显式学习方法无法解决small disjunction问题,而Lazy方法分类效率低。针对这两类方法存在的问题,提出了一种基于混合策略的关联分类方法。具体算法为:先判断待分类样本是否满足显式学习模式的分类器特征;然后把满足分类器特征的待分类样本用显式模式进行分类,把不满足分类器特征的待分类样本用Lazy模式来预测;最后结合两类方法的分类结果得到最终的分类结果。实验比较了该方法与传统的关联分类方法,结果表明,该方法在分类准确率和执行效率方面均达到了更好的效果。     

基于重叠盒覆盖算法的节点重要度评估

由于单一的节点评估方法存在不足,在融合复杂网络的局部特征以及全局特征前提下,提出了一种基于重叠盒覆盖算法的节点重要度评估方法.该方法利用重叠中心性对网络中的节点进行重要度排序,并且与其他不同中心性方法在复杂网络数据集中的节点排序方法进行比较;利用susceptible-infected (SI)模型模拟不同中心性方法前10个节点的传播能力,在此基础上以肯德尔系数进行比较,肯德尔系数越大表明相关性越高.实验结果表明,与其他中心性方法相比,重叠中心性得到的初始节点集合的累积平均感染能力高于其他中心性方法,并且与SI模型具有较高的相关性,该方法对于节点重要度评估是有效并且可行的.     

基于特征选择的多侧面覆盖算法

多侧面覆盖算法对海量高维数据的分类采用分而治之的思想,依据分量差的绝对值和,选取部分属性构建不同样本子集的覆盖,降低了学习的复杂度,但初始属性集的选择依据经验或实验获得。为降低初始属性集选择的主观性和属性集调整的复杂性,利用Relief特征选择方法确定适合不同数据集的最优特征子集,构建了分层递阶的覆盖网络,并对实际数据集进行实验。实验结果表明,该算法具有较高的精度和效率,可以有效地实现复杂问题的分类。     

一种基于ISOMAP的分类算法

提出一种解决分类任务的等测距映射算法,该算法利用类标签信息指导高维数据的降维．首先根据类标签在属于某个类的数据集上构造类内邻域图;然后寻找类间最短距离相邻边,并将其乘以大于1的尺度变化因子,使得降维后的类内数据更加紧凑、类问数据更加分开;最后利用BP神经网络构建一个近似的从原始高维数据集到低维数据集之间的映射函数,通过遗传算法对BP神经网络的初始权值和阈值进行优化,以避免使用剃度下降算法所带来的局部最优问题．实验结果表明,分类性能有较大提高,并对噪声有一定的鲁棒性．     

基于改进交叉覆盖设计算法的鲁棒性

在对交叉覆盖设计算法进行分析的基础上,针对该算法所显示的鲁棒性不强的问题,给出了一种改进的方法。模拟实验表明了这种改进能够明显地提高神经网络的泛化能力。     

A hybrid graph–neural method for domain decomposition

A hybrid method is presented for domain decomposition employing a graph theoretical algorithm and a neural network optimization model. The method uses graph theory and neural networks for decomposition of structured finite element meshes. Examples are presented to illustrate the efficiency of the developed method.     

基于混合神经网络的文本分类方法

为更好解决卷积神经网络提取特征不充分,难以处理长文本结构信息和捕获句子语义关系等问题,提出一种融合CNN和自注意力BiLSTM的并行神经网络模型TC-ABlstm.对传统的卷积神经网络进行改进,增强对文本局部特征的提取能力;设计结合注意力机制的双向长短期记忆神经网络模型来捕获文本上下文相关的全局特征;结合两个模型提取文...     

一种新的基于DNS协议的IPSec隐蔽通信研究

针对IPSec网络适应性差的问题,并结合隐蔽通信的技术,提出了一种新的结合IPSec隧道和DNS隧道的IPSec over DNS协议的VPN思路,并给出了协议实现的结构和流程。实验数据表明,两种技术的综合,为用户提供了一种新的更加可靠的网络安全解决方案。     

基于贝叶斯方法的决策树分类算法

针对数据挖掘的特点和本质,充分利用贝叶斯方法和决策树分类的优点,将贝叶斯的先验信息方法与决策树分类的信息增益方法相结合,提出了一种新的数据挖掘分类算法(BD1.0算法),并对此算法进行了设计和分析。实验分析表明,该算法可以处理不一致或者不完整数据等“脏数据”,比单纯使用贝叶斯方法或决策树方法具有更高的准确率,而且与C4.5算法具有近似的时间复杂度。     

基于交叉覆盖算法的时间序列模式匹配

为了有效进行时间序列的匹配,提出了基于交叉覆盖算法的模式匹配方法。首先对时间序列进行预处理,使它们分布在n+1维空间中某个中心在原点的球面上,再通过学习构造分类器,实现序列的准确匹配。主要讨论完全匹配查询,即把两两相似的时间序列分别归类,所以利用交叉覆盖算法准确分类的功能可以完成所探讨的问题。     

基于覆盖算法的条件信息熵表示及属性约简

利用覆盖算法对数据进行处理,得到论域U的一个划分,定义一种基于覆盖的条件信息熵,由新的条件信息熵定义新的属性重要性,并证明了对于一致决策表,它与代数定义下的重要性是等价的。以新的属性重要性为启发信息设计约简算法,并给出计算新的条件信息熵的算法。实验结果表明该约简算法能快速搜索到最优或次优约简。     

A genetic method for designing TSK models based on objective weighting: application to classification problems.

This paper proposes a genetic-based algorithm for generating simple and well-defined Takagi-Sugeno-Kang (TSK) models. The method handles several attributes simultaneously, such as the input partition, feature selection and estimation of the consequent parameters. The model building process comprises three stages. In stage one, structure learning is formulated as an objective weighting optimization problem. Apart from the mean square error (MSE) and the number of rules, three additional criteria are introduced in the fitness function for measuring the quality of the partitions. Optimization of these measures leads to models with representative rules, small overlapping and efficient data cover. To obtain models with good local interpretation, the consequent parameters are determined using a local MSE function while the overall model is evaluated on the basis of a global MSE function. The initial model is simplified at stage two using a rule base simplification routine. Similar fuzzy sets are merged and the “don’t care” premises are recognized. Finally, the simplified models are fine-tuned at stage three to improve the model performance. The suggested method is used to generate TSK models with crisp and polynomial consequents for two benchmark classification problems, the iris and the wine data. Simulation results reveal the effectiveness of our method. The resulting models exhibit simple structure, interpretability and superior recognition rates compared to other methods of the literature.     

基于多示例学习框架的文本分类算法

针对有特殊结构的文本,传统的文本分类算法已经不能满足需求,为此提出一种基于多示例学习框架的文本分类算法。将每个文本当作一个示例包,文本中的标题和正文视为该包的两个示例;利用基于一类分类的多类分类支持向量机算法,将包映射到高维特征空间中;引入高斯核函数训练分类器,完成对无标记文本的分类预测。实验结果表明,该算法相较于传统的机器学习分类算法具有更高的分类精度,为具有特殊文本结构的文本挖掘领域研究提供了新的角度。