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101.
102.
针对人类行为模型系统中存在大量的复杂任务关系和过程,系统输出的概率不确定性等问题,提出了基于Mamdani-Zadeh推理范式和D-S证据理论的建模方法.这种方法把输入空间分割成人们比较容易理解和表达问题的小区域,使用Mamdani-Zadeh和模糊D-S规则控制信度函数的输出值,从而得到了用户预期焦化元的清晰值,实现了模型系统关系、过程简单化;并通过具体例子证实了此方法的精确性和有效性。 相似文献
103.
基于云模型定性规则推理的分类方法 总被引:1,自引:0,他引:1
根据粗糙集原理和模糊集理论,提出了一种基于云模型定性规则推理的分类方法,利用云的相关理论获得多条件单规则中包含隶属度的决策表,结合模糊模式识别技术进行样本分类。针对一些数据对象分别隶属于不同类别的情况,用定性概念来代替模糊集中的定量数据并建立二元关系,能对连续型数据进行更为简单合理的"软"分类,从而使基于定性概念的算法模型符合人类思维方式。 相似文献
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105.
Pilsung Kang Author Vitae Author Vitae 《Pattern recognition》2008,41(11):3507-3518
Instance-based learning (IBL), so called memory-based reasoning (MBR), is a commonly used non-parametric learning algorithm. k-nearest neighbor (k-NN) learning is the most popular realization of IBL. Due to its usability and adaptability, k-NN has been successfully applied to a wide range of applications. However, in practice, one has to set important model parameters only empirically: the number of neighbors (k) and weights to those neighbors. In this paper, we propose structured ways to set these parameters, based on locally linear reconstruction (LLR). We then employed sequential minimal optimization (SMO) for solving quadratic programming step involved in LLR for classification to reduce the computational complexity. Experimental results from 11 classification and eight regression tasks were promising enough to merit further investigation: not only did LLR outperform the conventional weight allocation methods without much additional computational cost, but also LLR was found to be robust to the change of k. 相似文献
106.
Sagar Chaki Edmund Clarke Natasha Sharygina Nishant Sinha 《Formal Methods in System Design》2008,32(3):235-266
This paper presents an automated and compositional procedure to solve the substitutability problem in the context of evolving software systems. Our solution contributes two
techniques for checking correctness of software upgrades: (1) a technique based on simultaneous use of over-and under-approximations
obtained via existential and universal abstractions; (2) a dynamic assume-guarantee reasoning algorithm—previously generated component assumptions are reused and altered on-the-fly to prove
or disprove the global safety properties on the updated system. When upgrades are found to be non-substitutable, our solution
generates constructive feedback to developers showing how to improve the components. The substitutability approach has been
implemented and validated in the ComFoRT reasoning framework, and we report encouraging results on an industrial benchmark.
This is an extended version of a paper, Dynamic Component Substitutability Analysis, published in the Proceedings of the Formal Methods 2005 Conference, Lecture Notes in Computer Science, vol. 3582, by the
same authors. This research was sponsored by the National Science Foundation under grant nos. CNS-0411152, CCF-0429120, CCR-0121547,
and CCR-0098072, the Semiconductor Research Corporation under grant no. TJ-1366, the US Army Research Office under grant no.
DAAD19-01-1-0485, the Office of Naval Research under grant no. N00014-01-1-0796, the ICAST project and the Predictable Assembly
from Certifiable Components (PACC) initiative at the Software Engineering Institute, Carnegie Mellon University. The views
and conclusions contained in this document are those of the authors and should not be interpreted as representing the official
policies, either expressed or implied, of any sponsoring institution, the US government or any other entity. 相似文献
107.
In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring
and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include
provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats
in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the
general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer
to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed
temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method,
provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time,
data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach. 相似文献
108.
Yolanda Blanco-Fernndez Jos J. Pazos-Arias Alberto Gil-Solla Manuel Ramos-Cabrer Martín Lpez-Nores Jorge García-Duque Ana Fernndez-Vilas Rebeca P. Díaz-Redondo Jesús Bermejo-Muoz 《Knowledge》2008,21(4):305-320
Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV. 相似文献
109.
110.
基于模糊Petri网的汽车故障诊断仿真研究 总被引:1,自引:0,他引:1
本文将Petri网和模糊推理相结合,建立故障诊断的模糊Petri网模型。其中,用FPN表示模糊产生规则,用Petri网的变迁激活规则进行故障诊断推理,从而分析出异常行为过程间的因果关系,推理出故障的原因及其可信度。以汽车故障诊断为例,建立了基于模糊Petri网的诊断模型。通过仿真分析,验证了模型的正确性和算法的有效性。 相似文献