AADL软件容错系统建模与评估

提出了一种解决软件客错系统的可靠性评估方法,该方法基于AADL,对嵌入式软件系统进行建模,详细分析了软件系统内部各种构件的各种错误状态和其之间的错误传播,构建了AADL软件系统错误模型,并根据基本的转换规则将其转化为广义随机Petri网模型,使用现有工具对其进行了计算,从而实现了软件客错系统的可靠性评估;以航空交通控制(ATC)为应用场景进行实验.根据经验数据适当的假设了部分构件的失效率,收到了较好效果.     

基于Levenberg-Marquardt神经网络的企业资信评估方法研究

企业资信评估问题是一个复杂的非线性问题,而神经网络技术可实现非线性关系的隐式表达。文章提出将基于Levenberg-Marquardt算法的多层前馈型神经网络用于资信评估,并通过MATLAB软件及其神经网络工具对其进行仿真计算。实验结果表明,企业资信神经网络评估模型收敛速度快,准确率较高,具有一定的实用价值。     

基于遗传算法的模糊神经网络公交到站时间预测模型研究

公交到站时间的预测受到诸多因素的影响,各种因素对预测准确度不可度量,很难采用传统数学模型进行建模解决。采用基于遗传算法的模糊神经网络模型对公交到站时间进行预测,该模型将遗传算法和模糊推理系统融入多层前馈神经网络中,并通过模糊规则的隶属度进行初始化与更新网络各个参数初始值,同时利用多子群自适应遗传算法进行宏观搜索,提高整个网络的寻优能力。模型以成都市某线路公交运行时间预测为例对其进行了模拟仿真,仿真结果表明基于遗传算法的模糊神经网络公交到站时间预测模型具有较高的准确性与可靠性。     

基于粒子群优化BP神经网络的可靠性评估模型

CPU的可靠性对计算机系统至关重要。针对神经网络等方法在可靠性分析与评估中参数优化困难、模型评估精度不够准确等问题,提出一种基于粒子群优化BP神经网络的可靠性评估模型。该模型利用由正弦映射优化的PSO算法对BP神经网络的权值和阈值进行优化,提高BP神经网络的收敛速度以及评估精度。基于CPU中各功能模块的可靠度,根据改进的BP神经网络模型建立CPU的可靠性评估模型,通过模型训练与测试完成对CPU的可靠性评估。通过对比实验,验证该模型对辐射环境下CPU可靠性评估的有效性和准确性。     

基于构件软件的可靠性通用模型及系统实现

本文给出了基于构件软件中的函数抽象,依据该抽象提出了基于构件软件的一个可靠性通用模型,并介绍了相应的可靠性分析原型系统的实现。通过此系统可以在软件开发的各个阶段对软件进行可靠性分析,实现基于构件软件开发全过程的可靠性跟踪和监控。     

基于本体的Web服务可靠性模型

可靠性是Web服务选择和组合的一个重要度量标准。针对Web服务发现机制中存在效率低下和查准率不高的问题,提出了一种新的Web服务可靠性评估方法—OntoRcl。该方法基于本体技术,分析了可靠性属性的度量标准及其关系,建立了Web服务可靠性本体模型并评佑了Web服务可靠性。该方法有助于可靠性知识域的管理和开发、Web服务的可靠性评估和预测, Web服务的自动选择和组合。     

软硬件综合AADL可靠性建模及分析方法

目前嵌入式系统广泛应用于航空电子、远程医疗、汽车电子等具有高可靠性要求的系统中。随着嵌入式系统的复杂度越来越高,为了保障系统的高可靠性需求,需要在系统开发的早期设计阶段对系统的可靠性进行分析评估,以提高系统的开发效率。嵌入式系统中软件、硬件功能的失效都会对系统可靠性产生影响,而AADL的可靠性模型缺乏对硬件构件错误的影响及传播机制进行刻画分析的能力。本文综合考虑软、硬件错误发生失效后对系统可靠性的影响,提出了一种面向系统架构级别的软硬件综合可靠性分析方法。该方法基于电子电路设计中事务级建模方法,扩展了AADL事务级错误模型的语法和语义,来支持AADL对硬件构件错误传播的硬件功能行为建模,在此基础上,利用AADL模型实例化机制实现对嵌入式系统可靠性建模,刻画了错误行为在硬件构件之间、软硬件构件之间的传播与影响。同时,定义了AADL硬件构件事务级错误模型到广义随机Petri网模型的映射规则,实现了系统软、硬件综合的可靠性行为仿真计算模型组合,支持嵌入式系统的软硬件综合可靠性分析。论文开发了软硬件综合可靠性建模与分析工具原型,并以某型飞机空气增压系统为例,在航空电子系统架构设计中进行尝试,验证了该方法在复杂嵌入式系统设计中进行软硬件综合可靠性分析的可行性与优越性。     

Web服务组合的可靠性动态评估模型*

为了合理、高效、动态地评估Web服务组合的可靠性,为服务请求者提供高质量的组合服务,提出了一个Web服务组合的可靠性动态评估模型。该模型对服务提供者发布至UDDI注册中心的Web服务进行语义预先处理,根据语义Web服务间的逻辑组合关系,基于预推理技术构造Web服务的自动组合框架,提出了Web服务的自动组合算法,建立Web服务组合方案的路径结构;利用随机Petri网对满足服务请求者需求的服务组合路径结构进行可靠性建模,结合在线获取的Web服务可靠性信息,对Web服务组合的可靠性进行动态评估。实验示例结果分析表明,提出的模型能确保Web服务组合方案的有效性和提高服务组合的效率,对Web服务组合的可靠性评估具有较强动态性和灵活适应性。     

一种AADL系统可靠性模型转换方法

在原有构件依赖关系的基础上,提出一种架构分析与设计语言(AADL)系统可靠性模型的转换方法。该方法对AADL嵌入式系统体系结构进行可靠性建模,实现AADL可靠性模型到广义随机Petri网(GSPN)可靠性计算模型的转换。研究表明,该方法使AADL可靠性模型向GSPN模型的转换规则更加完备,能对嵌入式系统的可靠性进行准确与全面的分析与评估。     

基于层次和数据流驱动的软件可靠性分配方法

为了提高软件可靠性分配的有效性, 提出了一种基于层次和数据流驱动的软件可靠性分配方法。该方法对传统的重要度、复杂度度量方法进行改进; 针对软件系统开发初期体系结构中系统模块层次关系及模块间数据流关系进行抽象, 形成体系结构形式化定义, 建立可靠性因子的度量准则及度量模型, 依据度量模型对可靠性进行分配。最后结合实例进行了分析和验证, 结果表明了该分配模型的有效性和可行性。     

基于构件的NHPP类软件可靠性增长模型的研究

随着基于构件的软件开发模式的迅速发展,传统的NHPP模型无法适应大型的基于软构件的新型软件开发模式.结合软件可靠性分析中的黑盒方法和白盒方法,提出一种基于构件的NHPP类软件可靠性增长模型,CBNHPP模型.该模型以可加模型为基础,实现了时间域模型和体系结构域模型的结合,克服了这两种技术无法同时考虑软件测试过程中的故障排除和软件体系结构的问题.由于同时考虑了更多因素,因此该模型具有更高的准确性.最后通过实验证明了CB-NHPP模型的有效性.     

Bayesian regularized neural network decision tree ensemble model for genomic data classification

Machine learning techniques have been widely applied to solve the classification problem of highly dimensional and complex data in the field of bioinformatics. Among them, Bayesian regularized neural network (BRNN) became one of the popular choices due to its robustness and ability to avoid over fitting. On the other hand, Bayesian approach applied to neural network training offers computational burden and increases its time complexity. This restricts the use of BRNN in an on-line machine learning system. In this article, a Bayesian regularized neural network decision Tree (BrNdT) ensemble model, is proposed to combat high computational time complexity of a classifier model. The key idea behind the proposed ensemble methodology is to weigh and combine several individual classifiers and apply majority voting decision scheme to obtain an efficient classifier which outperforms each one of them. The simulation results show that the proposed method achieves a significant reduction in time complexity and maintains high accuracy over other conventional techniques.     

软件可靠性评估中 UML 应用

在开放环境下,软件规模日趋扩大,结构更加多元化,传统的基于状态的软件可靠性评估方法,状态空间膨胀增加了计算复杂度,而且不能对多种典型的系统结构进行很好的描述。为此,对传统的方法进行了改进,用UML的用例图分解系统,序列图描述子系统,并都作为软件可靠性分析的输入,通过自底向上的方法评估软件的可靠性,符合当前大规模复杂结构的软件系统可靠性评估。     

基于构件影响因子的软件可靠性评估方法

为提高构件式软件系统可靠性评估的准确性,使软件系统的优化效率得到提高,提出一种基于构件影响因子的软件可靠性评估方法.基于构件式软件系统具有的复杂网络特性,使用引入构件转移概率的加权PageRank算法评估构件的影响因子,将构件的影响因子引入到离散时间马尔科夫链的可靠性评估模型中,评估软件系统的可靠性.实验结果表明,该方...     

Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study

The prediction accuracy and generalization ability of neural/neurofuzzy models for chaotic time series prediction highly depends on employed network model as well as learning algorithm. In this study, several neural and neurofuzzy models with different learning algorithms are examined for prediction of several benchmark chaotic systems and time series. The prediction performance of locally linear neurofuzzy models with recently developed Locally Linear Model Tree (LoLiMoT) learning algorithm is compared with that of Radial Basis Function (RBF) neural network with Orthogonal Least Squares (OLS) learning algorithm, MultiLayer Perceptron neural network with error back-propagation learning algorithm, and Adaptive Network based Fuzzy Inference System. Particularly, cross validation techniques based on the evaluation of error indices on multiple validation sets is utilized to optimize the number of neurons and to prevent over fitting in the incremental learning algorithms. To make a fair comparison between neural and neurofuzzy models, they are compared at their best structure based on their prediction accuracy, generalization, and computational complexity. The experiments are basically designed to analyze the generalization capability and accuracy of the learning techniques when dealing with limited number of training samples from deterministic chaotic time series, but the effect of noise on the performance of the techniques is also considered. Various chaotic systems and time series including Lorenz system, Mackey-Glass chaotic equation, Henon map, AE geomagnetic activity index, and sunspot numbers are examined as case studies. The obtained results indicate the superior performance of incremental learning algorithms and their respective networks, such as, OLS for RBF network and LoLiMoT for locally linear neurofuzzy model.     

Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

This paper proposes an artificial neural network (ANN) based software reliability model trained by novel particle swarm optimization (PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.      

基于随机Petri网的软件可靠性分析方法

软件可靠性模型对于软件可靠性估测起着核心作用.目前提出的模型大多数都不能很好的适应复杂多变的应用环境的要求.针对构件化软件提出了一种基于随机Petri网的软件可靠性分析方法,它符合尽可能在软件开发的上游阶段对软件进行可靠性评估的思想.使用该方法建立起的模型可以很好的描述软件系统的动态变化过程,尽可能多的考虑了影响软件可靠性的因素,有利于降低软件可靠性描述与分析的复杂度,并可以得到软件系统处于各个状态的瞬时及稳态概率.     

Heuristic configuration of single hidden-layer feed-forward neural networks

For optimum statistical classification and generalization with single hidden-layer neural network models, two tasks must be performed: (a) learning the best set of weights for a network of k hidden units and (b) determining k, the best complexity fit. We contrast two approaches to construction of neural network classifiers: (a) standard back-propagation as applied to a series of single hidden-layer feed-forward nerual networks with differing number of hidden units and (b) a heuristic cascade-correlation approach that quickly and dynamically configures the hidden units in a network and learns the best set of weights for it. Four real-world applications are considered. On these examples, the back-propagation approach yielded somewhat better results, but with far greater computation times. The best complexity fit, k, for both approaches were quite similar. This suggests a hybrid approach to constructing single hidden-layer feed-forward neural network classifiers in which the number of hidden units is determined by cascade-correlation and the weights are learned by back-propagation.     

一种基于组件的应用虚拟化方法

远程显示技术是应用虚拟化系统中的关键技术,它的好坏将直接影响系统性能与用户体验。在目前的应用虚拟化系统中,远程显示多采用图像传输协议,易产生比较大的网络流量。针对该问题,提出一种基于组件的应用虚拟化方法。为源应用创建组件树模型,将该模型解析同构为Web组件模型,使源应用界面同构为Web界面展现在虚拟端,让用户在虚拟端的外设操作传输到源端模拟执行,源应用界面的变化以组件为数据元传输到虚拟端进行更新,以保证虚拟端与源端之间的同步。将基于组件的虚拟化模型与基于图像的虚拟化模型进行对比,实验结果表明,基于组件的虚拟化方法能够产生较少的网络流量。