Fully implicational methods for approximate reasoning based on interval-valued fuzzy sets

The aim of this paper is to discuss the approximate rea- soning problems with interval-valued fuzzy environments based on the fully implicational idea. First, this paper constructs a class of interval-valued fuzzy implications by means of a type of impli- cations and a parameter on the unit interval, then uses them to establish fully implicational reasoning methods for interval-valued fuzzy modus ponens （IFMP） and interval-valued fuzzy modus tel- lens （IFMT） problems. At the same time the reversibility properties of these methods are analyzed and the reversible conditions are given. It is shown that the existing unified forms of α-triple I （the abbreviation of triple implications） methods for FMP and FMT can be seen as the particular cases of our methods for IFMP and IFMT.

Application of Uncertainty Reasoning Theory to Satellite Fault Detection and Diagnosis

Reasoning theories are divided into certainty reasoning theories and uncertainty reasoning theories. Now, only certainty reasoning theories are used to detect and diagnose satellite faults. However, in practice, it is difficult to detect and diagnose some faults of the satellite automatically only by use of certainty reasoning theories. The reason is that detection and diagnosis of these faults require a rational reasoning and a fault tolerant capability. Fortunately, uncertainty reasoning theories can meet these requirements. It is attracting attention of many experts in the space field all over the world that uncertainty reasoning theories are applied to detect and diagnose satellite faults. Uncertainty reasoning theories include several kinds of theories, such as inclusion degree theory, rough set theory, evidence reasoning theory, probabilistic reasoning theory, fuzzy reasoning theory, and so on. Inclusion degree theory, rough set theory and evidence reasoning theory are three advanced ones. Based on these three theories respectively, the author introduces three new methods to detect and diagnose satellite faults in this paper. It is shown that the methods, suitable for detecting and diagnosing satellite faults, especially uncertainty faults, can remedy the defects of the current methods.

Air target intention recognition and causal effect analysis combining uncertainty information reasoning and potential outcome framework

Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.

Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach

Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.

An Approach of Reasoning with Uncertain Knowledge

A numeric reasoning method has been established.With this method,computations oflogical values are only needed instead of step-by-step matching reasoning.Reasoning time ofthis model is at most O(n^2).

Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks

With the rapid development of the semantic web and the ever-growing size of uncertain data,representing and reasoning uncertain information has become a great challenge for the semantic web application developers.In this paper,we present a novel reasoning framework based on the representation of fuzzy PR-OWL.Firstly,the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning,incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory,and introduces fuzzy PR-OWL,an Ontology language based on OWL2.Fuzzy PROWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation.Secondly,the paper explains the integration of the Fuzzy Probability theory and the Belief Propagation algorithm.The influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy PR-OWL.After that,the reasoning process,including the SSFBN structure algorithm,data fuzzification,reasoning of fuzzy rules,and fuzzy belief propagation,is scheduled.Finally,compared with the classical algorithm from the aspect of accuracy and time complexity,our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity,which proves the feasibility and validity of our solution to represent and reason uncertain information.

QUALITATIVE REASONING BY COMPUTING WITH WORDS IN HERRERA-MARTíNEZ’S LINGUISTIC MODEL

Most of modern systems for information retrieval, fusion and management have to deal with more and more qualitative information （by linguistic labels） besides information expressed quantitatively （by numbers）, since human reports are better and easier expressed in natural language than with numbers. In this paper, Herrera-Martfnez＇s 2-Tuple linguistic representation model is extended for reasoning with uncertain and qualitative information in Dezert-Smarandache Theory （DSmT） framework, in order to overcome the limitations of current approaches, i.e., the lack of precision in the final results of linguistic information fusion according to 1-Tuple representation （ q1 ）- The linguistic information which expresses the expert＇s qualitative beliefs is expressed by means of mixed 2 Tuples （equidistant linguistic labels with a numeric biased value）. Together with the 2-Tuple representation model, some basic operators are presented to carry out the fusion operation among qualitative information sources. At last, through simple example how 2-Tuple qualitative DSmT-based （q2 DSmT） fusion rules can be used for qualitative reasoning and fusion under uncertainty, which advantage is also showed by comparing with other methods.

A KIND OF FUZZY LINEAR PROGRAMMING PROBLEMS BASED ON INTERVAL-VALUED FUZZY SETS

The objective of this paper is to deal with a kind of fuzzy linear programming problem based on interval\|valued fuzzy sets (IVFLP) through the medium of procedure that turns IVFLP into parametric linear programming via the mathematical programming.Some useful results for the benefit of solving IVFLP are expounded and proved,developed and discussed.Furthermore,that the proposed techniques in this paper allow the decision\|maker to assign a different degree of importance can provide a useful way to efficiently help the decision\|maker make their decisions.

基于危险天气不确定性的最小风险路径规划方法

为降低飞行过程中遭遇危险天气的概率,同时避免大范围绕飞导致的路径与耗油增加,针对航路中的雷暴、积冰和颠簸天气,使用数值预报和概率预报,面向航前飞行计划,提出一种基于危险天气不确定性的最小风险路径规划方法.首先,基于概率预报数据使用配料法和C-F模型计算雷暴发生概率,基于数值预报数据计算积冰预测指数和颠簸预测指数;然后,融合多类型危险天气,提出一种具备风险标识的栅格化地图;在此基础上,改进传统路径最短的规划算法,构建以风险最小化为目标的Dijkstra和A^(*)算法;最后,使用2023年4月3日华中地区强对流天气预测数据建立风险地图,使用上述改进算法与传统Dijkstra、A^(*)和RRT算法进行路径规划并对比分析.结果表明,传统Dijkstra和A^(*)算法可计算得到最短飞行路径,而改进的A^(*)算法可计算得到总风险最小路径;若综合考虑飞行风险与路径长度,改进的Dijkstra算法最为适合.

基于不确定知识图谱嵌入的多关系近似推理模型

针对大规模知识图谱(KG)的不确定性嵌入模型中无法对多种逻辑关系进行近似推理的问题,提出一种基于不确定KG嵌入(UKGE)的多关系近似推理模型UDConEx(Uncertainty DistMult(Distance Multiplicative) and complex Convolution Embedding)。首先,UDConEx结合DistMult和ComplEx(Complex Embedding)模型的特点,使得UDConEx具有推理对称与非对称关系的能力;其次,UDConEx采用卷积神经网络(CNN)捕获不确定性KG中的交互信息,使它具有推理逆关系和传递关系的能力;最后,UDConEx利用神经网络对KG的不确定信息进行置信度学习,在UKGE空间中可以进行近似推理。在CN15k、NL27k和PPI5k这3个公开数据集上的实验结果表明,相较于MUKGE(Multiplex UKGE)模型,UDConEx在CN15k、NL27k和PPI5k的置信度预测任务中平均绝对误差(MAE)分别降低了6.3%,30.1%和44.9%;在关系事实排名任务中,基于线性的归一化折损累计增益(NDCG)在CN15k和NL27k数据集中分别提升了5.8%和2.6%;在多关系近似推理任务中验证了UDConEx具有多种逻辑关系的近似推理能力。UDConEx弥补了传统嵌入模型无法进行置信度预测的不足,实现了对多种逻辑关系的近似推理,具有更精确、具有可解释性的不确定性知识图谱推理能力。

动态不确定因果图在中医智能辅助辨证中的应用研究

以郁病辨证为例,开展动态不确定因果图在中医智能辅助辨证中的应用研究,提高中医智能辅助辨证模型中辨证知识的可视化程度和辨证推理过程的可解释性。对郁病权威文档进行整理分析、获取郁病辨证知识,采集郁病医案并进行筛选和数据预处理,构建郁病智能辅助辨证DUCG知识库,在知识库中表示症状知识和证型知识以及二者之间的关系,结合DUCG推理机进行辨证推理测试和分析。构建了包含19个子图的郁病智能辅助辨证DUCG知识库和包含6个子图的郁病智能辅助辨证DUCG核心知识库,辨证推理测试获得的初步准确率可达72.92%、按证型分组统计的准确率最高可达100%,可根据DUCG化简图对辨证结果进行详细解释。将动态不确定因果图理论应用于中医智能辅助辨证研究,有助于提高辨证模型中辨证知识的可视化程度和辨证推理过程的可解释性。

A Model for Uncertainty Knowledge

A numeric reasoning method is proposed in this paper. It transforms the step--by-step matching reasoning method into computations of logical values. The reasoning time of this model is at most O(n2).

非充分激励条件下路面附着系数估计算法

针对路面条件和车辆状态激励程度的不确定性导致的路面附着系数算法收敛速度和估计精度下降的问题,提出了一种基于模糊工况自适应强跟踪卡尔曼滤波的路面附着系数估计算法。利用模糊推理方法评估当前车辆运动状态的激励程度并输出协方差调整系数,引入强跟踪因子对标准卡尔曼滤波算法进行实时修正,通过及时调整路面附着系数的协方差的方式提高估计算法收敛速度,同时强跟踪因子保证算法对来自路面不确定的扰动具有鲁棒性。采用控制器硬件在环试验台的方式对所提算法的估计效果进行了验证,实验结果表明:所提出估计方法能够在车辆状态大激励程度条件时快速收敛到真值附近,小激励程度时降低估计值波动幅值,比强跟踪卡尔曼滤波算法和标准卡尔曼滤波算法在算法收敛速度和估计精度方面有明显提升。

网络安全态势预测技术研究综述

网络安全态势的感知、评估和预测已成为信息安全领域的重点研究方向。网络安全态势预测技术根据攻击事件的种类、频率等及关联融合后的态势值,利用过去时间的攻击事件信息及态势值对未来发展趋势进行预测,能够为网络管理人员提供必要的数据及信息。梳理常用的网络安全态势预测技术,分为不确定推理理论、机器学习方法、神经网络方法三大类;对每种网络安全态势预测技术进行分析对比,总结其优缺点;整理归纳在传统预测技术上的改进与优化,并讨论目前存在的问题及其未来的发展方向。

多主体市场下的园区综合能源系统随机鲁棒运行优化

园区综合能源系统(PIES)是提升消费侧能效水平的重要方案,同时其运行将受到市场价格不确定性的影响,需要在运行优化中予以考虑。但由于配电市场的参与主体较多,价格形成机制多样,准确刻画市场运作和价格波动特征极为困难。考虑多主体配电市场交易公平性基本原则,提出一种基于配电网风险中性定价的PIES随机鲁棒运行优化方法。首先,在风险中性测度下,类比资产价格过程,建立分布式能源系统和配电网运营商的售电定价模型,以确定市场环境下的配电网售电合理价格;其次,基于该合理价格,采用箱型不确定集等方法刻画配电网运营商售电价格以及源荷不确定性,建立了PIES两阶段随机鲁棒运行优化模型,基于列与约束生成算法和强对偶理论实现了模型求解;最后,算例结果验证了配电网售电合理价格模型的有效性和必要性、随机鲁棒运行优化方法相较传统随机优化和鲁棒优化方法的优点,以及市场电价不确定性对系统运行结果的影响。

不完全信息下的重大事故应急决策方法研究

为解决不完全信息下的重大事故应急决策问题,本文提出一种基于D-S证据理论的应急决策方法。首先,分析了重大事故现场关键事故情景和事故情景征兆之间的逻辑关系,并构建了事故证据集;其次,基于D-S证据理论构建了重大事故现场态势预测模型,并依据合成规则,按照时间顺序对重大事故现场得到的不完全信息进行不精确推理,实现了对重大事故关键情景的态势预测和事故应急决策;最后,通过实例验证了方法的可行性。

基于知识Petri网的确定性和不确定性联合推理

知识推理是人工智能的核心领域,旨在研究如何从已知(知识库和推理规则)推理出未知,以帮助智能体做出科学决策.而智能体所处的环境存在不可观性和不确定性,因此知识库通常不仅包含确定性知识,还包含不确定性知识,而且推理过程需要两类知识紧密协作.然而,目前的推理方法无法将两类知识统一表示,常常将两者对应的推理过程割裂进行.基于此,为了实现在统一的模型架构下完成确定性和不确定性联合推理,给出了一种知识Petri网推理方法.首先,定义了一种新的知识Petri网,使其不仅能够描述确定性的知识规范,也可以描述先验概率知识;其次,根据知识Petri网的网结构,给出了一种知识Petri网概率独立剪枝算法,能够指数级地降低不确定性推理的计算复杂性;最后,利用知识Petri网及其概率独立剪枝算法,给出了一种新型推理算法,实现了确定性和不确定性的联合推理,并利用Wumpus世界进行了演示和验证.

信息生态视角下森林火灾网络舆情风险预警研究

针对不确定信息下森林火灾网络舆情风险预警问题,构建基于证据推理的直觉模糊风险预警评估模型.首先,从信息生态系统的角度,识别出森林火灾事件网络舆情风险指标体系;其次,针对指标信息的不确定性和不完整性,基于模糊规则通过直觉模糊数统一描述多种指标信息;然后,利用直觉模糊熵和模糊层次分析法联合确定各级指标权重,并采用证据推理理论完成指标信息集结融合;最后,通过直觉模糊测距确定网络舆情风险预警等级,并通过效用值法确定网络舆情风险排序.通过3个现实案例分析和验证所建模型的实用性和有效性,为森林火灾网络舆情风险评估研究提供新思路和新方法.

Necessary Conditions of Two-Level Uncertainty Reasoning Model (URM) and the Improvement on It

Based on the previous work, some necessary conditions of the two-level Uncertainty Reasoning Model (URM) are proposed and an improvement on the twcalevel Uan is made that can describe and process the deviation. In addition, the paper presents two theorems for specifying the correctness about the improvement. Finally, the application of the twrvlevel URM is discussed.

包含度理论

不确定性是复杂系统的特征。在人工智能与专家系统中，不确定性的研究越来越具有重要意义。Ｚａｄｅｈ［１］于１９６５年提出模糊集，把经典集合扩充到模糊集合，从而解决了“对象”的不确定性。本文引进了包含度的概念，解决了“关系”的不确定性。同时指出，包含度不仅是各种不确定性推理方法的概括，而且解决了知识获取与矛盾规则排除两个重要问题。