A Fuzzy Reasoning System and Its Heuristic Inference Algorithm

Based on a presented inference algorithm of fuzzy reasoning, a fuzzy reasoning system is made up. A method of modeling the fuzzy reasoning system, and the setting up of the reasoning knowledge based and reasoning rules are studied in this paper. Then a heuristic inference algorithm is presented according to the system.

Case Based Reasoning Intelligent System for Network Computer Aided Process Planning

Computer aided process planning system played a key role for integrating design and manufacturing or assembly systems properly considering available resources and design constraints.To take advantage of the enterprise resource,the web CAPP framework was established.Case based reasoning and multi agent system were integrated in the system.The multi agent mecha- nism was discussed in the paper.And an instance of case base was introduced.They made the system run independently and contin- uously in the network environment of process planning problems.

Knowledge Representation and Fuzzy Reasoning of an Agricultural Expert System

The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert system and artificial intelligence, an in-depth analysis and summary are made of the knowledge features of die agricultural multimedia expert system and data models involved. According to the practical problems in agricultural field, the architectures and functions of the system are designed, and some design ideas about the hybrid knowledge representation and fuzzy reasoning are proposed.

Immune Recognition Method Based on Analogy Reasoning in Intrusion Detection System

In this paper, we propose an analogy based immune recognition method that focuses on the implement of the clone selection process and the negative selection process by means of analogy similarity. This method is applied in an IDS （Intrusion Detection System） following several steps. Firstly, the initial abnormal behaviours sample set is optimized through the combining of the AIS （Artificial Immune System） and the genetic algorithm. Then, the abnormity probability algorithm is raised considering the two sides of abnormality and normality. Finally, an intrusion detection system model is established based on the above algorithms and models.

Visualization of Approximate Reasoning Systems

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Application of Case-Based Reasoning to Intelligent Support System for Coordinating Land Conflicts

To establish the institutional mechanism for land conflict coordination in China, a case-based reasomng system is developed as an intelligent support and effective manner to resolve such issues. The establishment of the case library is discussed, previous land conflict cases are archived in a structural representation format for retrieval, and the similarity algorithm is adopted to compare the case features. Group tests show a good classification performance, which reveals that the system is feasible.

基于SHEL+Reason模型的住院药房药品调剂差错安全风险综合评价指标体系的建立

目的建立基于SHEL+Reason模型的住院药房药品调剂差错安全风险综合评价指标体系。方法通过SHEL+Reason模型对我院住院药房2019年7—12月期间的441076条医嘱进行分析,结合文献复习法形成药品调剂差错安全风险的专家咨询表,采取德尔菲法(Delphi Method)进行咨询,通过统计学分析对指标体系进行完善,运用综合评分法构建综合评价方程,抽取2021年1—6月期间的897条调剂差错医嘱进行评价研究,采用信度分析验证指标体系的可靠性,对综合评价结果采用X-σ_(X)、X、X+σ_(X)确定区分不同等级的界限,进行单因素方差分析(完全随机设计),验证通过综合评价方法获得的各分类间的差异是否有统计学意义。结果本指标体系包含4个一级指标,10个二级指标,进行了两轮专家咨询后专家权威性和指标体系内部一致性良好,利用抽取的医嘱验证了构建的综合评价指标体系具有良好的区分度。结论建立的住院药房药品调剂安全风险评价指标体系适用于医院住院药房的药品调剂差错管理,可为医院相关管理工作的开展提供参考。

Research on and implementation of a knowledge-reasoning based evaluation support system

An evaluation support system involving complicated decision making problems during engineering design of products is introduced by first describng and modeling complicated decision making problems, and then constructing and describing the architecture and functional structure of an evaluation support system, based on knowledge-based reasoning. Knowledge contains important experience of field-expert and can be classified and stored in knowledge bases, and therefore, the system suggests information-processing tools based on information resources including data knowledge bases and methods bases, which can be used to evaluate the designs against the multi-criteria decision framework thereby providing decision-makers with rational and scientific information.

A Dialogue System for Coherent Reasoning with Inconsistent Knowledge Bases

Traditionally, the AI community assumes that a knowledge base must be consistent. Despite that, there are many applications where, due to the existence of rules with exceptions, inconsistent knowledge must be considered. One way of restoring consistency is to withdraw conflicting rules;however, this will destroy part of the knowledge. Indeed, a better alternative would be to give precedence to exceptions. This paper proposes a dialogue system for coherent reasoning with inconsistent knowledge, which resolves conflicts by using precedence relations of three kinds: explicit precedence relation, which is synthesized from precedence rules;implicit precedence relation, which is synthesized from defeasible rules;mixed precedence relation, which is synthesized by combining explicit and implicit precedence relations.

基于暂态关键特征逻辑推理的复杂电网响应驱动暂态稳定性判别

暂态稳定实时性判别是响应式稳定控制的核心,新能源和直流输电并网下电网的结构、运行和响应特征复杂程度骤增,现有实时性判别方法的准确性和泛化性面临挑战。该文针对含新能源和直流输电的复杂电网暂态稳定性,提出了一种基于可量测暂态能量特征的自适应逻辑推理判稳方法,实现无电网模型依赖且具有可解释性的实时性稳定性判别。首先,基于发电机转子运动的能量关系,结合特勒根定理构建了面向复杂电网响应信息的能量函数并论证了其守恒性;然后,根据系统动能-势能能量转换特征定义了稳定预判因数,并结合最大功角差构成了判稳关键特征量,提出了基于自适应模糊推理神经网络(ANFIS)的关键特征量与稳定状态之间的映射模型,实现了暂态稳定性实时推理评估;最后,在简单系统中量化分析了关键特征量与系统稳定性间的关系,并在修改后含新能源与直流输电的IEEE10机39节点系统中验证了该文所提方法的有效性与泛化性。

Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

半定量信息工业控制异构网络安全评估

工业控制系统的异构性及其与互联网深度融合给工控系统安全评估带来精度不高等诸多问题.本文提出一种基于半定量信息的工业异构网络安全态势评估模型,该模型可以提高评估精度,解决难以描述不确定性信息和难以契合工控异构网络复杂结构等现存问题.该方法将工业控制网络划分为不同异构子网,根据子网特点选取有针对性的评估属性,并使用性能优秀的ER规则对不同类型的评估属性进行融合,可以有效利用定性知识和定量数据,获得更加准确的评估结果.在多个实际工业控制网络数据集上进行评估实验得到的结果表明:本文提出的模型与网络安全评估实际参考值的均方根误差值比RF、BP、SVM模型分别降低了80.2%、40.8%和87.7%,验证了该模型的有效性和准确性.

融合深度神经网络和方面感知的可解释推荐方法

为提升推荐结果的准确性和可解释性,提出一种融合深度神经网络和方面感知的可解释推荐方法。针对评分数据的稀疏性问题,综合考虑显式和隐式评分数据,通过深度神经网络的矩阵分解模型学习用户和物品的潜在特征;通过无监督的方面提取模块来学习用户和物品的方面特征;将潜在特征和方面特征统一到预测层进行评分预测;针对生成解释质量低且缺乏个性化的问题,在评分预测的基础上,采用提取的主题词和预定义的神经模板相结合生成推荐理由,提高解释的生成质量。实验表明,提出的方法不仅能准确预测用户对物品的评分,还能够生成具有解释性的推荐理由,且生成的解释质量优于对比方法。

英国富尔顿文官制度改革及失败原因

“二战”后,英国僵化的文官制度阻碍了经济发展,并面临着严峻的外部挑战。哈罗德·威尔逊上台后任命富尔顿进行文官改革,力图解决文官的“通才”原则弊端、人数激增以及分类庞杂等问题。1968年《富尔顿报告》发布,提出改变文官制度任用方式,建设文官部和文官学院,改进文官晋升制度,推动了英国现代文官制度建设。但是由于《富尔顿报告》脱离实际引起了保守势力的反对、文官部和文官学院权力遭到侵蚀、政府更迭等原因,最终影响了改革进程。

基于IQPSO-GA优化ANFIS模型的服务器故障预警方法

针对服务器底层部分业务类硬件故障对系统稳定运行的影响,提出一种改进的量子行为粒子群优化(IQPSO)与遗传算法(GA)相结合的混合元启发式优化算法对自适应神经模糊推理系统(ANFIS)参数进行训练,以获得更准确的ANFIS规则进行硬件故障预警的方法;首先,通过分析服务器业务与硬件相关参数之间的映射关系,通过采集的数据集对ANFIS模型进行训练构造预测模型;其次,考虑ANFIS在梯度计算过程中存在容易陷入局部最优值的问题,设计了一种IQPSO算法结合GA中的交叉和变异算子操作混合元启发算法全局搜索ANFIS规则参数;最后,通过一组后处理样本数据集对所提方法有效性和稳定性进行了检验;实验结果表明,该方法可有效预警服务器硬件故障,基于所提混合元启发优化算法获得的ANFIS模型具备更快的收敛速度和更高的全局搜索精度,与传统ANFIS模型相比泛化精度提高了47%以上。

数字孪生多模态视觉推理的神经-符号系统

面对数字孪生在多模态视觉数据融合中的异质性和动态性挑战,提出一种结合深度学习与符号智能的方法。该方法通过深度神经网络对视觉数据进行实时解析,并借助符号系统存储的知识和事件响应规则,实现对复杂推理过程的自主管理。为提高系统对物理世界变化的适应性,提出一种融合多模态信息和外部知识的增强推理机制,该机制能有效地整合来自传感器的实时数据和历史知识库中的信息,以支持更加准确和合理的决策制定。以退役锂电池拆解过程为案例验证表明,该方法不仅能够在多模态数据环境中实现高准确率的识别和分析,还能够基于推理机制生成合理且逻辑一致的操作建议,有效提升了拆解效率和安全性。

基于改进FP-Growth算法和贝叶斯的营业线施工安全风险分析

铁路营业线施工事故致因因素众多且存在关联关系,挖掘事故致因之间的关联关系和因果关系对事故的预防管控具有重要意义。通过文本挖掘对2010—2022年某路局营业线事故调查报告进行处理,提取出51个事故致因因素。基于事故因果连锁理论将致因因素分为人因层、设备层、环境层、管理层4个层级进行分层分析,构建铁路营业线施工事故致因体系。基于压缩算法和差分编码对传统FP-Growth算法进行改进,以此对铁路营业线施工事故致因进行挖掘,找到满足提升度要求的高支持度关联规则和高置信度关联规则,发现关键致因关联和事故致因规律。基于贝叶斯网络理论、致因关联关系和专家经验建立营业线施工安全风险贝叶斯网络,结合复杂网络理论分析网络节点度、聚类系数与节点介数等特征,找到关键致因因素。在此基础上,运用因果推理和故障诊断推理进一步剖析营业线施工过程中的高风险致因,并从“人防、物防、技防”3方面提出预防管控措施。案例结果表明:施工人员操作不当、施工造成接触网故障、施工导致设施设备侵限、施工作业损害电缆设备、施工造成轨道电路故障和施工、检修、清扫设备耽误列车类事故之间的关联关系较为频繁,且为红光带事故的高概率致因,在施工作业过程中应多层次重点预防管控。研究成果为铁路营业线施工安全管理提供一种新的风险分析方法。

基于专家系统的地铁列车火灾智能决策系统研究

地铁列车狭长封闭,发生火灾时容易造成人员伤亡,及时正确的应急决策是降低人员损失的重要一环。通过把专家系统引入列车火灾应急决策可以缩短响应时间,提高现场人员处置能力。首先,系统以到站列车火灾、列车区间隧道发生火灾继续到站或无法到站3种场景为载体,分析了不同线路条件、不同驾驶模式以及不同车辆状态对于人员应急处置的影响,并基于专家知识和经验构建了列车火灾智能决策系统目标集和知识规则集。其次,系统以正向推理为控制策略,通过列车控制和管理系统(TCMS)获得列车子系统实时状态,并与知识规则集相互匹配,形成了基于规则的推理机制和推理逻辑。最后,应用C++扩展软程序模拟输入变量,对系统知识规则和推理逻辑进行了有效验证。

考虑作战行动的单兵系统体系贡献率评估

针对单兵系统体系贡献率评估过程中存在的未考虑作战任务及评估信息不确定性等问题,提出了基于作战行动的两级证据推理评估方法,从基于能力需求的视角进行作战任务分解和能力分析,建立了作战能力需求指标体系;建立两级置信规则库,构建作战活动与能力需求的映射矩阵,采用证据推理进行能力满足度、作战任务完成率的计算,获得了单兵系统的体系贡献率,最后基于城市作战战场想定,给出了分析实例。基于作战行动的两级证据推理评估方法,对于单兵系统体系贡献率评估具有针对性和有效性,也为装备体系贡献率评估提供了一种新思路。