ANALYSIS OF CIRCUIT TOLERANCE BASED ON RANDOM SET THEORY

Monte Carlo Analysis has been an accepted method for circuit tolerance analysis, but the heavy computational complexity has always prevented its applications. Based on random set theory, this paper presents a simple and flexible tolerance analysis method to estimate circuit yield. It is the alternative to Monte Carlo analysis, but reduces the number of calculations dramatically.

Modeling Multisource-heterogeneous Information Based on Random Set and Fuzzy Set Theory

This paper presents a new idea, named as modeling multisensor-heterogeneous information, to incorporate the fuzzy logic methodologies with mulitsensor-multitarget system under the framework of random set theory. Firstly, based on strong random set and weak random set, the unified form to describe both data (unambiguous information) and fuzzy evidence (uncertain information) is introduced. Secondly, according to signatures of fuzzy evidence, two Bayesian-markov nonlinear measurement models are proposed to fuse effectively data and fuzzy evidence. Thirdly, by use of "the models-based signature-matching scheme", the operation of the statistics of fuzzy evidence defined as random set can be translated into that of the membership functions of relative point state variables. These works are the basis to construct qualitative measurement models and to fuse data and fuzzy evidence.

Autonomous Clustering Using Rough Set Theory

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.

RANDOM SETS: A UNIFIED FRAMEWORK FOR MULTISOURCE INFORMATION FUSION

The more diverse the ways and means of information acquisition are,the more complex and various the types of information are. The qualities of available information are usually uncertain,vague,imprecise,incomplete,and so on. However,the information is modeled and fused traditionally in particular,name some of the known theories: evidential,fuzzy sets,possibilistic,rough sets or conditional events,etc. For several years,researchers have explored the unification of theories enabling the fusion of multisource information and have finally considered random set theory as a powerful mathematical tool. This paper attempts to overall review the close relationships between random set theory and other theories,and introduce recent research results which present how different types of information can be dealt with in this unified framework. Finally,some possible future directions are discussed.

A NEW METHOD FOR CONSTRUCTING FUZZY EVIDENCE BASED ON THE NON-CONSONANT RANDOM SET

Natural-language information is often mathematically expressed by fuzzy sets. With the random set theory as a bridge, this kind of information can be transformed into fuzzy evidence in Dempster-Shafer (DS) theory. Then Dempster's combination rule or other combination rules of evi- dence can be used perfectly for fusing natural-language and other information. However, this traditional transformation involves the use of α -cutsets to construct the focal elements which have to be repre- sented as consonant sets. This construction is very inflexible and unreasonable in some practical ap- plications. In this paper, with the desire to overcome this limitation, a method for constructing more general non-consonant focal elements is proposed based on the random set theory. Some examples are given to show the generality and the efficiency of this new method. Finally, we validate that non-consonant constructions provide less degrees of total uncertainty than that of the consonant case in these examples by using the evaluation criterion of total uncertainty.

A NEW APPROACH OF SIMULTANEOUS FAULTS DIAGNOSIS BASED ON RANDOM SETS AND DSMT

Simultaneous faults often occur in running equipments, in order to solve the problems of the simultaneous faults, a new approach based on random sets and Dezert-Smarandache Theory (DSmT) is proposed in this paper. Firstly, the simultaneous faults' model is built based on the generalized frame of discernment in DSmT. Secondly, according to the unified description of combination rules in evidence reasoning based on random sets, a new combination rule for simultaneous faults diagnosis is proposed. Thirdly, according to the working characteristics and environment of the sensors used to acquire fault characteristic information, a new method to construct basic probability assignment function is pro- posed based on membership. Finally, diagnosis result is obtained by use of the new combination rule combined with decision rules. A case pertaining to the fault diagnosis for a multi-function rotor test-bed is given, and the result shows that the proposed diagnosis approach is feasible and efficient.

AN INTRUSION DETECTION SYSTEM BASED ON EVIDENCE THEORY AND ROUGH SET THEORY

In this paper,we propose a novel Intrusion Detection System (IDS) architecture utilizing both the evidence theory and Rough Set Theory (RST). Evidence theory is an effective tool in dealing with uncertainty question. It relies on the expert knowledge to provide evidences,needing the evidences to be independent,and this make it difficult in application. To solve this problem,a hybrid system of rough sets and evidence theory is proposed. Firstly,simplification are made based on Variable Precision Rough Set (VPRS) conditional entropy. Thus,the Basic Belief Assignment (BBA) for all evidences can be calculated. Secondly,Dempster’s rule of combination is used,and a decision-making is given. In the proposed approach,the difficulties in acquiring the BBAs are solved,the correlativity among the evidences is reduced and the subjectivity of evidences is weakened. An illustrative example in an intrusion detection shows that the two theories combination is feasible and effective.

基于RST的连退炉温度神经网络模型研究

准确的连续退火炉温度控制是高质量冷轧生产的基本要求,然而变量的不确定性和不完整性以及测量误差的存在使这项任务具有挑战性。人工神经网络在这一领域虽然得到了广泛的应用,但是它们可能无法提供所需的准确性。随着深度置信网络、堆叠自编码机等算法的出现,使得越来越多的学者倾向于对无监督—微调模型的研究。人们寄期望于无监督模型可以挖掘出数据中潜在的关系和知识,然后通过微调引导,从而得到一个更好、更具鲁棒性的模型。基于该思想,将粗糙集理论引入退火炉的神经网络“遗忘门”部分,通过粗糙集理论决定变量的淘汰和保留。

一种联合EMD-RST的HRRP-RATR

针对高分辨雷达目标信号非平稳特性,提出了一种基于经验模态分解(Empirical mode decomposition,EMD)特征提取技术的雷达目标高分辨一维距离像自动目标识别(HRRP-ATR)方法。该方法首先利用EMD将雷达目标HRRP分解为多个平稳的内禀分量(Intrinsic mode function,IMF),分别采用AR模型、奇异值分解、能量分布进行特征提取。为缩减特征维数,引入粗糙集理论(RST)进行属性约简、规则提取以及分类,并同基于支持向量机(SVM)的分类方法进行比较,得到一些有价值的结论。

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

基于张量低秩补全算法的极端天气短期负荷预测

高效准确的短期电力负荷预测对提升新型电力系统经济运行十分重要。针对极端天气场景下负荷预测数据量较少、随机性较强的特点,提出一种基于张量低秩补全算法的短期负荷预测模型,并选取极端高温场景展开研究。首先,给出极端天气定义,并基于改进型炎热指数和气温两项指标进行数据筛选;其次,提出一种基于张量的负荷数据补全模型,补全缺失数据;然后,通过Pearson相关性分析筛选输入特征量,构建基于长短时记忆(long short term memory, LSTM)网络和粗糙集理论(rough set theory, RST)的LSTM-RST短期负荷预测模型;最后,以苏州某地实际负荷数据设置算例进行验证,仿真结果表明,所提短期负荷预测方法具有较高的准确性。

Research on extended AHP method with the aid of RST

Analytic Hierarchy Process (AHP) method can be used to solve the tasks of multi-criterion decision system fields, but some complicated questions processed by AHP cannot be easily solved by means of the general method. It is because of being unsatisfied with consistency condition or judgment matrix too intricate to solve, which causes AHP invalidation. So in order to resolve this problem, AHP knowledge systems reduced with the aid of Genetic Algorithms (GA) were proposed, which directly acquired the order of AHP issue through the rule of Rough Sets Theory (RST) method, or solved the tasks reduced by RST with classical AHP method. On this condition, the compare decision system of region informatization level was solved, and the results solved were the same to those by classical AHP, which denoted that this method was more simple and reliable, besides the four rules of changing AHP system into RST Decision System.

INTERVAL ANALYSIS OF FUZZY-RANDOM HEAT CONDUCTION IN COMPOSITE TUBES

During the analysis of stability heat conduction in the composite tubes, firstly, when the temperature boundary conditions are the random conditions, equations of the mean values and variances of the random thermal function are transformed. Secondly, when the heat conduct parameters are the fuzzy numbers and the temperature boundary conditions are the random numbers, interval equations of the heat conduction are presented. Thirdly, by comparison of the interval results, the result in the interval analysis is larger than that in the confidence interval. Moreover the error expecting equation is presented. Finally, with upper （lower） approximation in rough set theory, a new method of the interval analysis to deal with the stability heat conduction is presented.

Keystroke Dynamics Based Authentication Using Information Sets

This paper presents keystroke dynamics based authentication system using the information set concept. Two types of membership functions (MFs) are computed: one based on the timing features of all the samples and another based on the timing features of a single sample. These MFs lead to two types of information components (spatial and temporal) which are concatenated and modified to produce different feature types. Two Component Information Set (TCIS) is proposed for keystroke dynamics based user authentication. The keystroke features are converted into TCIS features which are then classified by SVM, Random Forest and proposed Convex Entropy Based Hanman Classifier. The TCIS features are capable of representing the spatial and temporal uncertainties. The performance of the proposed features is tested on CMU benchmark dataset in terms of error rates (FAR, FRR, EER) and accuracy of the features. In addition, the proposed features are also tested on Android Touch screen based Mobile Keystroke Dataset. The TCIS features improve the performance and give lower error rates and better accuracy than that of the existing features in literature.

基于RS-SSA-RF的帷幕灌浆施工质量预测

灌浆施工质量预测作为施工过程控制的重要抓手,为寻求具有高精度及低耗时性的灌浆施工质量预测方法,建立了基于粗糙集理论和樽海鞘群算法优化的随机森林的灌浆施工质量预测模型。通过工程应用,对帷幕灌浆施工质量进行了预测分析,并与支持向量机、BP神经网络对比,结果显示本文所提出的方法耗时仅为219.313 s,预测值与实测值的Pearson相关系数为0.936、平均绝对误差为0.140、均方误差为0.037、平均绝对百分比误差为0.059,与实际值具有一致性。研究表明,所建立的模型可为灌浆施工质量控制提供参考。

基于随机森林算法和粗糙集理论的改进型深度学习短期负荷预测模型

精准的电力负荷预测有利于保障电力系统的安全、经济运行。针对现行预测算法存在的预测准确度低、模型耗时长等问题,提出一种基于随机森林(random forest,RF)算法和粗糙集理论(rough set theory,RST)的改进型深度学习(deeplearning, DL)短期负荷预测模型(RF-DL-RST)。该模型首先基于历史数据,利用随机森林算法提取影响负荷预测的关键特征量;然后将关键特征量和历史负荷值作为深度神经网络的输入、输出项进行训练,并通过粗糙集理论修正预测结果。最后,通过算例进行仿真验证,结果表明,该模型的预测准确度比单一的深度学习模型及不进行预测修正的模型更高。

基于位姿图优化的势均衡多伯努利滤波器SLAM方法

在复杂室内环境下,传统基于随机有限集理论的SLAM方法存在机器人位姿精度低、计算量大的问题。针对此问题,提出一种基于位姿图优化的势均衡多伯努利滤波器SLAM方法。首先,该方法通过势均衡多伯努利滤波器获得地图特征估计,避免了数据关联。其次,提出了自适应信息控制法,丰富先验信息。然后,通过自适应信息控制法将位姿图优化理论与势均衡多伯努利滤波器SLAM结合,优化机器人的位姿估计。最后,进行实验对比分析,结果表明所提方法比RB-PHD-SLAM方法有更好的SLAM精度及实时性。

基于随机集理论的并发故障诊断信息融合方法

为了诊断并发故障,提出一种基于随机集理论的信息融合方法。首先构造包含并发故障的论域,并在此论域的超幂集上定义扩展型随机集。基于该随机集和广义集值映射给出证据组合规则的随机集模型,用其构造可以同时适用于单发和并发故障诊断的新型组合规则。此外,根据传感器提供的故障信息构造故障样板模式与待检模式的模糊隶属度函数,利用模糊集的随机集表示以及随机集似然测度,获得两种模式匹配的程度作为待融合的诊断证据。最后通过在电机柔性转子平台上的试验,证明了所提方法可有效地减少单一传感器信息诊断的不确定性,显著提高转子系统故障诊断的精度。

基于随机集含糊证据的风力发电机故障诊断方法

针对大型风力发电机故障信息的复杂性和不确定性,提出了基于随机集信任测度和似真测度的含糊化方法。该方法将故障样本模式和故障待检模式进行含糊化,利用随机集的信任测度和似真测度对待检模式和样本模式进行匹配,实现数据级融合;然后将匹配的结果作为证据理论的信任函数和似真函数,进行特征级融合;最后将融合结果作为决策级证据理论的证据进行最终融合,从而得出诊断结果。风力发电机的算例验证了该方法的合理性和有效性。

基于随机集理论的多源信息统一表示与建模方法

由于信息形式的多样性及其特征的复杂性,使得对不确定、未知性、非精确和不完全等类型信息的表示和建模都要通过相应的方法完成,由于方法的不统一,从而很难实现对异类信息的融合.所以,能否找到一种统一的理论实现多源异类信息的表示与建模,最终实现融合成为信息融合中的关键问题.众多研究者经过多年的探索发现,随机集理论有望解决这个难题.本文首先对各种多源信息进行分类,并介绍几种常用的表示和建模方法及其适用范围;随后引入随机集理论的基本概念和性质,综合论述该理论与已有方法之间的相互转化关系,并进一步论证用随机集统一表示和建模多源信息的可能性;最后,介绍随机集理论在信息融合中的应用并指出未来的发展方向.