共查询到20条相似文献,搜索用时 15 毫秒
1.
Indika Meedeniya Irene Moser Aldeida Aleti Lars Grunske 《Software and Systems Modeling》2014,13(4):1395-1415
Probabilistic models are commonly used to evaluate quality attributes, such as reliability, availability, safety and performance of software-intensive systems. The accuracy of the evaluation results depends on a number of system properties which have to be estimated, such as environmental factors or system usage. Researchers have tackled this problem by including uncertainties in the probabilistic models and solving them analytically or with simulations. The input parameters are commonly assumed to be normally distributed. Accordingly, reporting the mean and variances of the resulting attributes is usually considered sufficient. However, many of the uncertain factors do not follow normal distributions, and analytical methods to derive objective uncertainties become impractical with increasing complexity of the probabilistic models. In this work, we introduce a simulation-based approach which uses Discrete Time Markov Chains and probabilistic model checking to accommodate a diverse set of parameter range distributions. The number of simulation runs automatically regulates to the desired significance level and reports the desired percentiles of the values which ultimately characterises a specific quality attribute of the system. We include a case study which illustrates the flexibility of this approach using the evaluation of several probabilistic properties. 相似文献
2.
RAY HARRIS 《International journal of remote sensing》2013,34(6):847-866
A probabilistic relaxation model is used to improve maximum likelihood classifications of LANDSAT data of arid and urban areas in and around Ai Jahra, Kuwait. The problems of urban pixels, the role of compatibility coefficients and the iterations of the model are presented and discussed. 相似文献
3.
Numerical vs. statistical probabilistic model checking 总被引:1,自引:0,他引:1
Håkan L. S. Younes Marta Kwiatkowska Gethin Norman David Parker 《International Journal on Software Tools for Technology Transfer (STTT)》2006,8(3):216-228
Numerical analysis based on uniformisation and statistical techniques based on sampling and simulation are two distinct approaches
for transient analysis of stochastic systems. We compare the two solution techniques when applied to the verification of time-bounded
until formulae in the temporal stochastic logic CSL, both theoretically and through empirical evaluation on a set of case
studies. Our study differs from most previous comparisons of numerical and statistical approaches in that CSL model checking
is a hypothesis-testing problem rather than a parameter-estimation problem. We can therefore rely on highly efficient sequential
acceptance sampling tests, which enables statistical solution techniques to quickly return a result with some uncertainty.
We also propose a novel combination of the two solution techniques for verifying CSL queries with nested probabilistic operators. 相似文献
4.
In this paper, a fuzzy Object-Oriented Data model (FOOD) is defined based on the extension of a Graph-based Object model (D. Lucarella and A. Zanzi “A graph-oriented data model,” in Database and Expert Systems Applications, R. Wagner and H. Toma, Eds., Springer-Verlag, Berlin, 1996, pp. 197–206), in order to manage both crisp and imperfect information. These capabilities are requisites of many current applications dealing with data of different nature and with complex interrelationships. The model is based on a visual paradigm which supports both the representation of the data semantics and the direct browsing of the information. In the extended model both the database scheme and instances are represented as directed labeled graphs in which the fuzzy and uncertain information has its own representation. ©1999 John Wiley & Sons, Inc. 相似文献
5.
通过一个实例分析比较了概率逻辑、主观概率逻辑、不确定逻辑和模糊逻辑的思想方法。提出了自己的观点:基于数据统计的概率逻辑是最科学的。不确定逻辑比主观概率逻辑更科学。当具有不确定性的原子命题具有独立性时,不确定逻辑和模糊逻辑的观点是一致的。而对于处理带有不确定性的相关性命题,不确定逻辑比模糊逻辑更科学。但是模糊逻辑在建立推理理论方面见长。 相似文献
6.
The probabilistic fuzzy set (PFS) is designed for handling the uncertainties with both stochastic and fuzzy nature. In this paper, the concept of the distance between probabilistic fuzzy sets is introduced and its metric definition is conducted, which may be finite or continuous. And some related distances are discussed. The proposed distance considers the random perturbation in progress by introducing the distance of probability distribution, thus it improves the ability to handle random uncertainties, and some inadequacy of the distance of probability distribution is remedied. Finally, a PFS-based distance classifier is proposed to discuss the classification problem, the numerical experiment shows the superiority of this proposed distance in fuzzy and stochastic circumstance. 相似文献
7.
Spatially enabled customer segmentation using a data classification method with uncertain predicates
Spatial attributes are important factors for predicting customer behavior. However, thorough studies on this subject have never been carried out. This paper presents a new idea that incorporates spatial predicates describing the spatial relationships between customer locations and surrounding objects into customer attributes. More specifically, we developed two algorithms in order to achieve spatially enabled customer segmentation. First, a novel filtration algorithm is proposed that can select more relevant predicates from the huge amounts of spatial predicates than existing filtration algorithms. Second, since spatial predicates fundamentally involve some uncertainties, a rough set-based spatial data classification algorithm is developed to handle the uncertainties and therefore provide effective spatial data classification. A series of experiments were conducted and the results indicate that our proposed methods are superior to existing methods for data classification. 相似文献
8.
Mining fuzzy association rules from uncertain data 总被引:3,自引:3,他引:0
Association rule mining is an important data analysis method that can discover associations within data. There are numerous
previous studies that focus on finding fuzzy association rules from precise and certain data. Unfortunately, real-world data
tends to be uncertain due to human errors, instrument errors, recording errors, and so on. Therefore, a question arising immediately
is how we can mine fuzzy association rules from uncertain data. To this end, this paper proposes a representation scheme to
represent uncertain data. This representation is based on possibility distributions because the possibility theory establishes
a close connection between the concepts of similarity and uncertainty, providing an excellent framework for handling uncertain
data. Then, we develop an algorithm to mine fuzzy association rules from uncertain data represented by possibility distributions.
Experimental results from the survey data show that the proposed approach can discover interesting and valuable patterns with
high certainty. 相似文献
9.
10.
This paper proposes a novel two-stage fuzzy classification model established by the fuzzy feature extraction agent (FFEA) and the fuzzy classification unit (FCU). At first, we propose a FFEA to validly extraction the feature variables from the original database. And then, the FCU, which is the main determination of the classification result, is developed to generate the if–then rules automatically. In fact, both the FFEA and FCU are fuzzy models themselves. In order to obtain better classification results, we utilize the genetic algorithms (GAs) and adaptive grade mechanism (AGM) to tune the FFEA and FCU, respectively, to improve the performance of the proposed fuzzy classification model. In this model, GAs are used to determine the distribution of the fuzzy sets for each feature variable of the FFEA, and the AGM is developed to regulate the confidence grade of the principal if–then rule of the FCU. Finally, the well-known Iris, Wine, and Glass databases are exploited to test the performances. Computer simulation results demonstrate that the proposed fuzzy classification model can provide a sufficiently high classification rate in comparison with other models in the literature. 相似文献
11.
Data uncertainty can be caused by numerous factors such as measurement precision limitations, network latency, data staleness and sampling errors. When mining knowledge from emerging applications such as sensor networks or location based services, data uncertainty should be handled cautiously to avoid erroneous results. In this paper, we apply probabilistic and statistical theory on uncertain data and develop a novel method to calculate conditional probabilities of Bayes theorem. Based on that, we propose a novel Bayesian classification algorithm for uncertain data. The experimental results show that the proposed method classifies uncertain data with potentially higher accuracies than the Naive Bayesian approach. It also has a more stable performance than the existing extended Naive Bayesian method. 相似文献
12.
Credit scoring analysis is an important activity, especially nowadays after a huge number of defaults has been one of the main causes of the financial crisis. Among the many different tools used to model credit risk, the recent development of rough set models has proved effective. The original development of rough set theory has been widely generalized and combined with other approaches to uncertain reasoning, especially probability and fuzzy set theories. Since coherent conditional probability assessments cope well with the problem of unifying these different approaches, a merging of fuzzy rough set theory with this subjectivist approach is proposed. Specifically, expert partial probabilistic evaluations are encompassed inside a gradual decision rule structure, with coherence of the conclusion as a guideline. In line with Bayesian rough set models, credibility degrees of multiple premises are introduced through conditional probability assessments. Nonetheless, discernibility with this method remains too fine. Therefore, the basic partition is coarsened by equivalence classes based on the arity of positively, negatively and neutrally related criteria. A membership function, which grades the likelihood of default, is introduced by a peculiar choice of t-norms and t-conorms. To build and test the model, real data related to a sample of firms are used. 相似文献
13.
Spatial information in autonomous robot tasks is uncertain due to measurement errors, the dynamic nature of the world, and an incompletely known environment. We present a probabilistic spatial data model capable of describing relevant spatial data, such as object location, shape, composition, and other parameters, in the presence of uncertainty. Uncertain spatial information is modeled through continuous probability distributions on values of attributes. The data model is designed to support our visual tracking and navigation prototype. 相似文献
14.
针对已有概率频繁项集挖掘算法采用模式增长的方式构建树时产生大量树节点,导致内存空间占用较大以及发现概率频繁项集效率低等问题,提出了改进的不确定数据频繁模式增长(PUFP-Growth)算法。该算法通过逐条读取不确定事务数据库中数据,构造类似频繁模式树(FP-Tree)的紧凑树结构,同时更新项头表中保存所有尾节点相同项集的期望值的动态数组。当所有事务数据插入到改进的不确定数据频繁模式树(PUFP-Tree)中以后,通过遍历数组得到所有的概率频繁项集。最后通过实验结果和理论分析表明:PUFP-Growth算法可以有效地发现概率频繁项集;与不确定数据频繁模式增长(UF-Growth)算法和压缩的不确定频繁模式挖掘(CUFP-Mine)算法相比,提出的PUFP-Growth算法能够提高不确定数据概率频繁项集挖掘的效率,并且减少了内存空间的使用。 相似文献
15.
We believe that nonlinear fuzzy filtering techniques may be turned out to give better robustness performance than the existing linear methods of estimation (H/sup 2/ and H/sup /spl infin// filtering techniques), because of the fact that not only linear parameters (consequents), but also the nonlinear parameters (membership functions) attempt to identify the uncertain behavior of the unknown system. However, the fuzzy identification methods must be robust to data uncertainties and modeling errors to ensure that the fuzzy approximation of unknown system's behavior is optimal in some sense. This study presents a deterministic approach to the robust design of fuzzy models in the presence of unknown but finite uncertainties in the identification data. We consider online identification of an interpretable fuzzy model, based on the robust solution of a regularized least-squares fuzzy parameters estimation problem. The aim is to resolve the difficulties associated with the robust fuzzy identification method due to lack of a priori knowledge about upper bounds on the data uncertainties. The study derives an optimal level of regularization that should be provided to ensure the robustness of fuzzy identification strategy by achieving an upper bound on the value of energy gain from data uncertainties and modeling errors to the estimation errors. A time-domain feedback analysis of the proposed identification approach is carried out with emphasis on stability, robustness, and steady-state issues. The simulation studies are provided to show the superiority of the proposed fuzzy estimation over the classical estimation methods. 相似文献
16.
This paper presents the expectation–maximization (EM) variant of probabilistic neural network (PNN) as a step toward creating
an autonomous and deterministic PNN. In the real world, faulty reading sensors can happen and will create input vectors with
missing features yet they should not be discarded. To overcome this, regularized EM is put in place as a preprocessing step
to impute the missing values. The problem faced by users when using random initialization is that they have to define the
number of clusters through trial and error, which makes it stochastic in nature. Global k-means is used to autonomously find the number of clusters using a selection criterion and deterministically provide the number
of clusters needed to train the model. In addition, fast Global k-means will be tested as an alternative to Global k-means to help reduce computational time. Tests are conducted on both homoscedastic and heteroscedastic PNNs. Benchmark medical
datasets and also vibration data collected from a US Navy CH-46E helicopter aft gearbox known as Westland were used. The tests’
results fully support the usage of fast Global k-means and regularized EM as preprocessing steps to aid the EM-trained PNN. 相似文献
17.
Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data 总被引:2,自引:0,他引:2
Xiang Lian Lei Chen 《The VLDB Journal The International Journal on Very Large Data Bases》2009,18(3):787-808
Reverse nearest neighbor (RNN) search is very crucial in many real applications. In particular, given a database and a query
object, an RNN query retrieves all the data objects in the database that have the query object as their nearest neighbors.
Often, due to limitation of measurement devices, environmental disturbance, or characteristics of applications (for example,
monitoring moving objects), data obtained from the real world are uncertain (imprecise). Therefore, previous approaches proposed
for answering an RNN query over exact (precise) database cannot be directly applied to the uncertain scenario. In this paper,
we re-define the RNN query in the context of uncertain databases, namely probabilistic reverse nearest neighbor (PRNN) query,
which obtains data objects with probabilities of being RNNs greater than or equal to a user-specified threshold. Since the
retrieval of a PRNN query requires accessing all the objects in the database, which is quite costly, we also propose an effective
pruning method, called geometric pruning (GP), that significantly reduces the PRNN search space yet without introducing any
false dismissals. Furthermore, we present an efficient PRNN query procedure that seamlessly integrates our pruning method.
Extensive experiments have demonstrated the efficiency and effectiveness of our proposed GP-based PRNN query processing approach,
under various experimental settings. 相似文献
18.
According to the soundness and completeness of information in databases,the expressive form and the semantics of incomplete information are discussed in this paper.On the basis of the discussion,the current studies on incomplete data in relational databases are reviewed.In order to represent stochastic uncertainty in most general sense in the real world,probabilistic data are introduced into relational databases.An extended relational data model is presented to express and manipulate probabilistic data and the operations in relational algebra based on the extended model are defined in this paper. 相似文献
19.
Using the robust design of a vehicle vibration model considering uncertainties can elaborately show the effects of those unsure values on the performance of such a model. In this paper, probabilistic metrics, instead of deterministic metrics, are used for a robust Pareto multi-objective optimum design of five-degree of freedom vehicle vibration model having parameters with probabilistic uncertainties. In order to achieve an optimum robust design against probabilistic uncertainties existing in reality, a multi-objective uniform-diversity genetic algorithm (MUGA) in conjunction with Monte Carlo simulation is used for Pareto optimum robust design of a vehicle vibration model with ten conflicting objective functions. The robustness of the design obtained using such a probabilistic approach is shown and compared with that of the design obtained using deterministic approach. 相似文献
20.
Supervised texture classification using a probabilistic neuralnetwork and constraint satisfaction model 总被引:9,自引:0,他引:9
The texture classification problem is projected as a constraint satisfaction problem. The focus is on the use of a probabilistic neural network (PNN) for representing the distribution of feature vectors of each texture class in order to generate a feature-label interaction constraint. This distribution of features for each class is assumed as a Gaussian mixture model. The feature-label interactions and a set of label-label interactions are represented on a constraint satisfaction neural network. A stochastic relaxation strategy is used to obtain an optimal classification of textures in an image. The advantage of this approach is that all classes in an image are determined simultaneously, similar to human perception of textures in an image. 相似文献