基于FMEA和模糊Petri网推理的装备保障过程诊断研究

为满足装备保障过程分析、瓶颈优化的需要,提出基于失效模式影响分析(FMEA)和模糊Petri网推理的装备保障过程诊断方法,通过FMEA建立装备保障过程诊断的因果图,由因果图确定保障过程诊断的推理规则,应用模糊Petri网建立智能的、利于计算机编程实现的保障过程诊断的过程模型。通过研究发现,基于FMEA的规则形成方法便于知识、经验向规则的准确转换提取,模糊Petri网的方法利于将推理过程形式化,实现推理的自动化,提高过程诊断的效率。研究的过程诊断模型和方法已在集群装备保障过程优化决策系统实现中取得较好的效果。     

基于直觉模糊Petri网的故障诊断

故障诊断经常受到多种不确定性和模糊性因素的影响,针对不确定性的故障诊断问题,利用直觉模糊集较好的表达不确定性信息的优势和Petri网较好的并行处理以及图形处理问题的能力,构建了直觉模糊Petri网模型。由于将直觉模糊推理转化为矩阵运算的过程中有非隶属度参数的参与,因此推理结果可提供更多的信息。根据实际故障诊断中的模糊推理问题,给出了带有权值、阈值等参数条件下新的直觉模糊推理算法。通过获取和处理故障诊断中的不确定性和模糊性的知识,该算法将故障诊断过程转化为利用直觉模糊Petri网的直觉模糊推理过程。实际燃气轮机故障诊断模型案例表明了所给直觉模糊推理算法的有效性。     

Extended event–condition–action rules and fuzzy Petri nets based exception handling for workflow management

Exception handling plays a key role in dynamic workflow management that enables streamlined business processes. Handling application-specific exceptions is a knowledge-intensive process involving different decision-making strategies and a variety of knowledge, especially much fuzzy knowledge. Current efforts in workflow exception management are not adequate to support the knowledge-based exception handling. This paper proposes a hybrid exception handling approach based on two extended knowledge models, i.e., generalized fuzzy event–condition–action (GFECA) rule and typed fuzzy Petri net extended by process knowledge (TFPN-PK). The approach realizes integrated representation and reasoning of fuzzy and non-fuzzy knowledge as well as specific application domain knowledge and workflow process knowledge. In addition, it supports two handling strategies, i.e., direct decision and analysis-based decision, during exception management. The approach fills in the gaps in existing related researches, i.e., only providing the capability of direct exception handling and neglecting fuzzy knowledge. Based on TFPN-PK, a weighted fuzzy reasoning algorithm is designed to address the reasoning problem of uncertain goal propositions and known goal concepts by combining forward reasoning with backward reasoning and therefore to facilitate cause analysis and handling of workflow exceptions. A prototype system is developed to implement the proposed approach.     

基于模糊框架-产生式知识表示及推理研究

知识表示是专家系统求解能力及正确性的基础。针对不同知识表示方法的局限性,采用框架与产生式知识表示法结合表示专家知识。同时鉴于传统知识表示及推理方法在描述事实生产中不确定知识及经验中的缺陷问题,将模糊推理与知识表示相结合,应用模糊因子,定量细化描述模糊知识;并结合知识表示特点应用动态加权平均匹配函数及模糊推理方法,提出基于模糊框架-产生式知识表示方法及推理的研究,量化地表示知识及推理过程,为决策人员提供更加直观、准确的推理依据。     

Surveillance multisensor management with fuzzy evaluation of sensor task priorities

In this contribution a fuzzy management scheme is proposed to cope with the evaluation of multisensor tasks priority in defence surveillance applications. Based on all fused track and sector data, a reasoning system determines the priority of each surveillance task to perform during the decision cycle, by means of a symbolic inference process inspired in the behaviour of human operators. This approach allows to integrate high-level information (possibly subjective concepts, considering also their uncertainty) with conventional numeric representations in the decision process. The elected formal method to represent the variables involved in this decision process is the theory of possibility and fuzzy sets, since it offers a unified framework to represent uncertainty knowledge. In this sense, to obtain the priority for each task, the reasoning process relies on a decision tree whose nodes are linguistic variables representing intermediate concepts used by a human operator to determine the tasks priorities. The validity of the fuzzy reasoning approach is supported by the fact that it has been able to manage environmental situations in a similar way as experienced human operators do. Included results illustrate how the importance of the tasks, measured through their time-varying priorities, allows the manager to timely adapt sensor operation to changing situations.     

一种不确定性规则挖掘的混合方法及其应用

为了在含有连续条件属性的决策表中挖掘和应用决策规则,通过采用互信息熵作为模糊聚类的截距值选定准则,对信息系统对象集进行模糊聚类划分,并根据粗糙集的有关概念,确定基于截距的近似集和边界,从中挖掘出不确定性决策规则,进一步建立决策知识库。最后以该知识库为识别模型,结合模糊隶属度的计算和不确定性推理进行地层识别,取得了令人满意的效果。     

模糊属性集的粗糙上下近似研究

在知识发现和数据挖掘领域,粗集理论与模糊集合理论都是研究信息系统中知识不完备、不准确问题,两者都可利用观测数据表达知识,进行推理。论文针对传统粗集环境下知识表示模型用固定的属性及属性值来描述对象这一局限,提出利用模糊属性模型对知识表达系统进行信息描述,并给出了模糊属性集的粗糙上下近似模型。     

D-S证据理论在决策支持系统中的应用

D-S证据理论提供了一种解决多数据源不确定信息推理和融合的有效方法。证据理论能够对各自独立的证据加以综合给出一致性结果,并能处理具有模糊和不确定信息的合成问题,最终达到信息互补。与其他推理方法相比更符合人类思维决策过程。为此,提出一种基于D-S证据理论的灾害决策支持方法,并根据试验结果验证了该方法的有效性和可行性。     

A probabilistic reasoning model: Formulation and control strategy

It has been recognized that past experiences of a decision maker often plays a pivotal role in solving new problem instances. Therefore, the ability to model human reasoning processes has become an important subject of research in recent years. In many applications, the reasoning process must deal with uncertainty inherent in the problem domain. This research addresses the issue of supporting the model formulation and data acquisition processes for situations that (i) operate under uncertain conditions, and (ii) utilize evidential information that is gathered in stages. A theoretical framework is presented for the probabilistic formulation of the reasoning process that incorporates past experiences. The model is validated by testing its performance on simulated data, and is shown to work well when a sufficiently large number of cases are available for estimating probabilities. The probabilistic reasoning system can revise beliefs in an intuitively appealing and theoretically sound manner when information is acquired in an incremental fashion. Two dynamic information gathering strategies are discussed for such a reasoning system, one using information theoretic techniques, and the other using decision theoretic techniques.     

Integrating SPOT-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices — The case of sugarcane harvest on Reunion Island

Time series of optical satellite images acquired at high spatial resolution is a potentially useful source of information for monitoring agricultural practices. However, the information extracted from this source is often hampered by missing acquisitions or uncertain radiometric values. This paper presents a novel approach that addresses this issue by combining time series of satellite images with information from crop growth modeling and expert knowledge. In a fuzzy framework, a decision support system that combines multi-source information was designed to automatically detect the sugarcane harvest at field scale. The formalism that we used deals with the imprecision of the data and the approximation of expert reasoning. System performances were analyzed using a time series of SPOT-5 images. Results obtained were in substantial agreement with ground truth data: overall accuracy reached 97.80% with stability values exceeding 89.21% for all decisions. The contribution of fuzzy sets to overall accuracy reached 15.08%. The approach outlined in this paper is very promising and could be very useful for other agricultural applications.     

移动P2P网络路由策略中模糊知识处理的研究

由于移动P2P网络的动态多变以及移动用户的频繁移动,以合作为基础的移动agent所携带的路由信息是不确定的,非实时的、模糊的.同时,这种交互得到的间接路由信息,更加具有不完整性甚至欺骗性,通过进行移动agent合作模型的设计和移动agent所携带路由信息的模糊知识处理的研究,并利用模糊知识处理方法学的支持,提出了一种从移动agent所携带的模糊知识中分析和过滤出较准确和有价值路由数据的算法,在模糊的路由知识中找到能如实反映网络的路由信息,以此提供移动节点以及移动agent使用,通过实验仿真证明,该算法有效减小了平均端到端时延和路由开销,增加了分组投递率和建立连接的移动节点数目,同时,可以较好处理移动agent中模糊的路由信息,对路由策略性能有较大的提高.     

A fuzzy group-preferences analysis method for new-product development

This paper reports a new idea-screening method for new product development (NPD) with a group of decision makers having imprecise, inconsistent and uncertain preferences. The traditional NPD analysis method determines the solution using the membership function of fuzzy sets which cannot treat negative evidence. The advantage of vague sets, with the capability of representing negative evidence, is that they support the decision makers with the ability of modeling uncertain opinions. In this paper, we present a new method for new-product screening in the NPD process by relaxing a number of assumptions so that imprecise, inconsistent and uncertain ratings can be considered. In addition, a new similarity measure for vague sets is introduced to produce a ratings aggregation for a group of decision makers. Numerical illustrations show that the proposed model can outperform conventional fuzzy methods. It is able to provide decision makers (DMs) with consistent information and to model situations where vague and ill-defined information exist in the decision process.     

A fuzzy group-preferences analysis method for new-product development

This paper reports a new idea-screening method for new product development (NPD) with a group of decision makers having imprecise, inconsistent and uncertain preferences. The traditional NPD analysis method determines the solution using the membership function of fuzzy sets which cannot treat negative evidence. The advantage of vague sets, with the capability of representing negative evidence, is that they support the decision makers with the ability of modeling uncertain opinions. In this paper, we present a new method for new-product screening in the NPD process by relaxing a number of assumptions so that imprecise, inconsistent and uncertain ratings can be considered. In addition, a new similarity measure for vague sets is introduced to produce a ratings aggregation for a group of decision makers. Numerical illustrations show that the proposed model can outperform conventional fuzzy methods. It is able to provide decision makers (DMs) with consistent information and to model situations where vague and ill-defined information exist in the decision process.     

基于工程决策的专家系统模型设计与研究

工程决策的影响因子和参数繁多且复杂,为提高工程决策的可用性和准确性提出基于工程决策的专家系统模型。模型根据现有数据模型结合实际需求,在关系数据内部形成知识支撑环境模型,采用框架和产生式规则相结合的方法,将推理过程与专家知识库结合,并提出一种综合评估方法为支持,辅助生产方案设计和措施调整。以油田实际生产现状为例,给出了相应油田异常井措施调整方案的推理过程,完成了模型的实例设计和实现,达到了提供辅助决策以提高决策可用性和准确性的目的。     

Fuzzy decision trees: issues and methods

Decision trees are one of the most popular choices for learning and reasoning from feature-based examples. They have undergone a number of alterations to deal with language and measurement uncertainties. We present another modification, aimed at combining symbolic decision trees with approximate reasoning offered by fuzzy representation. The intent is to exploit complementary advantages of both: popularity in applications to learning from examples, high knowledge comprehensibility of decision trees, and the ability to deal with inexact and uncertain information of fuzzy representation. The merger utilizes existing methodologies in both areas to full advantage, but is by no means trivial. In particular, knowledge inferences must be newly defined for the fuzzy tree. We propose a number of alternatives, based on rule-based systems and fuzzy control. We also explore capabilities that the new framework provides. The resulting learning method is most suitable for stationary problems, with both numerical and symbolic features, when the goal is both high knowledge comprehensibility and gradually changing output. We describe the methodology and provide simple illustrations.     

A Fuzzy Inference Network Model for Search Strategy Using Neural Logic Network

In this paper, a fuzzy inference network model for search strategy using neural logic network is presented. The model describes search strategy, and neural logic network is used to search. Fuzzy logic can bring about appropriate inference results by ignoring some information in the reasoning process. Neural logic networks are powerful tools for the reasoning process but not appropriate for the logical reasoning. To model human knowledge, besides the reasoning process capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct a fuzzy inference network model based on the neural logic network, extending the existing rule inference network. And the traditional propagation rule is modified.     

不确定性的综合描述及其智能决策支持系统

为了克服单一模型描述的不确定的片面性,提出了描述不确定性的综合描述模型,并给出了在智能决策支持系统中的应用。首先在不确定性内涵的阐释的基础上,建立了不确定性的综合描述模型。然后给出了基于不确定性的综合描述模型的不确定知识表示和推理方法。最后给出了基于综合描述模型的智能决策支持系统的体系结构。不确定性的综合描述模型具有全面客观描述不确定性的优点。     

一个基于神经网络的专家系统

医疗专家系统主要使用基于知识的技术，其中的决策规则和策略来自于人类的专家。把这些知识和各种推理方法结合，可以建立一个模拟专家决策过程的系统。建立这样一个系统，需要经常与专家磋商，以获取专家的知识，因而需要大量的时间和精力。为此，本文提出直接从数据中提取有效的信息，即用神经网络提取隐含在大量数据中对医疗诊断有效的信息，继之与基于规则的知识，各种推理方法相结合，建立一个神经网络专家系统。