‘Useful’ fuzzy measures of information,integrated ambiguity and directed divergence

In the present paper, a new concept of ‘useful’ fuzzy information, based on utility, is introduced by considering the uncertainties of fuzziness and probabilities of random events. A ‘useful’ fuzzy measure of integrated ambiguity is obtained by integration of fuzzy and probabilistic uncertainties with utility. A new ‘useful’ measure of fuzzy-directed divergence of a fuzzy set from another fuzzy set is proposed and its validity proved. Finally, the constrained optimisation of ‘useful’ fuzzy entropy and ‘useful’ fuzzy-directed divergence is studied.     

Absolute stability analysis for a class of Takagi–Sugeno fuzzy control systems

This article presents absolute stability conditions for a particular class of Takagi–Sugeno fuzzy control systems. Initially, a Takagi–Sugeno fuzzy control system is transformed into a multivariable Lur’e type system. A simple algorithm for checking the absolute stability of this system is then proposed. Since the key of the proposed algorithm is to solve algebraic Riccati equations, software packages such as MATLAB provides a simple means to check the conditions. The proposed approach does not limit the methods of fuzzification and defuzzification. This article presents several analytical examples to verify the simplicity and efficiency of the proposed approach.     

一种基于Attention-GRU和iForest的周期性时间序列异常检测算法

对数据中异常模式的检测（异常检测）是数据分析领域一个非常重要的研究方向,尤其对时间序列的异常检测是其中的一个难点。目前,关于时序数据异常检测的研究有很多,如利用滑动窗口、小波分析、概率图模型、循环神经网络等不同技术来进行检测,但是在处理问题时还存在或多或少的不足,无法保证实际工程中的实时效率和准确性。针对周期性时间序列异常检测问题,提出一种基于Attention-GRU和iForest的异常检测算法,根据带有注意力机制的循环神经网络构建模型,实现预测长序列数据,保证工程检测效率,并通过iForest建立正常数据波动区间,避免了使用假设检验造成的误差。经实践验证,该算法能够提高实际工程中的周期性时序数据异常检测效率,并有较好的召回率和准确率。     

基于自相关函数的模糊时间序列优化算法

针对模糊时间序列模型中模糊推理规则的优化问题, 提出一种时间序列的自相关理论与模糊时间序列相结合的算法. 首先考查数据平稳化; 然后运用传统的数据模糊化方法得到模糊集, 进而建立模糊规则, 并运用自相关函数理论对模糊规则进行优化; 最后通过对Alabama 大学注册人数的预测验证了所提出算法的有效性.

     

Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural networks and genetic algorithms have been used in fuzzy time series method to improve the method. While fuzzy clustering and genetic algorithms are being used for fuzzification, artificial neural networks method is being preferred for using in defining fuzzy relationships. In this study, a hybrid fuzzy time series approach is proposed to reach more accurate forecasts. In the proposed hybrid approach, fuzzy c-means clustering method and artificial neural networks are employed for fuzzification and defining fuzzy relationships, respectively. The enrollment data of University of Alabama is forecasted by using both the proposed method and the other fuzzy time series approaches. As a result of comparison, it is seen that the most accurate forecasts are obtained when the proposed hybrid fuzzy time series approach is used.     

Effects of constructing fuzzy discretization from crisp discretization for rule-based classifiers

Crisp discretization is one of the most widely used methods for handling continuous attributes. In crisp discretization, each attribute is split into several intervals and handled as discrete numbers. Although crisp discretization is a convenient tool, it is not appropriate in some situations (e.g., when there is no clear boundary and we cannot set a clear threshold). To address such a problem, several discretizations with fuzzy sets have been proposed. In this paper we examine the effect of fuzzy discretization derived from crisp discretization. The fuzziness of fuzzy discretization is controlled by a fuzzification grade F. We examine two procedures for the setting of F. In one procedure, we set F beforehand and do not change it through training rule-based classifiers. In the other procedure, first we set F and then change it after training. Through computational experiments, we show that the accuracy of rule-based classifiers is improved by an appropriate setting of the grade of fuzzification. Moreover, we show that increasing the grade of fuzzification after training classifiers can often improve generalization ability. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

一种新的扩展模糊概念数据模型

为了对现实世界中存在的不精确、不确定的信息进行建模,学者们提出了各种不同的扩展关系数据模型.基于可能性理论,本文对概念数据模型IFO的不同层次在模糊信息环境下进行了扩展,提出了一种新的模糊IFO数据模型FIFO,该模型能够有效地表达和处理概念数据建模层次中的模糊数据.文章重点讨论了FIFO中对象和关系（主要是ISA关系）的模糊性,并给出了FIFO模型的各个层次所对应的图形表达.     

Effect of fuzzification on the plosive cognition system

Fuzzy algorithms provide a simpler and more powerful approach than statistical decision methods for describing non-ideal (fuzzy) environments in which there exists no precise boundary between the categories due to inherent vagueness rather than randomness. This paper attempts to demonstrate the effectiveness of such an algorithm when applied to the computer recognition of patterns of biological origin such as Telugu unaspirated plosives in initial position of large number of utterances in CVC context. A multieategorizer is described in which the fuzzy processor embodies a fuzzy property extractor and a similarity matrix generator. A provision fur controlling fuzziness in property sets had been made by keeping two parameters. ‘exponential’ and ‘denominational’ fuzzifiers, in the components of property matrices ; their effect on recognition score is also studied.

Machines’ performances are explained by plotting curves and through confusion matrices when transition, duration and slope of transition from the point of transient release of stop closure to the steady state of only first two formants were used as input features. Voiced stops are differentiated more easily than unvoiced stops, with the maximum overall recognition score ranging from 60% for dentals to 85% for bilabials. The fuzzy hedge ‘ slightly ’ when applied to property sets reduces the confusion from that of the hedge ‘ very ’ and consecutive utilizations of the operations ‘CONT’, ‘ OIL’ and ‘INT’ resulted in a wide variation of about 20 to 25% in the recognition score. Such a variation is found to be insignificant beyond an optimum value of the exponential fuzzifier’.     

基于滑动窗口的直升机序列异常检测算法

无标签的序列在异常检测算法中往往存在着对数据的信息掌握不全面、不能合理使用的情况,而采用深度学习的技术实现检测时往往对其计算的解释性欠佳;对于攻克这些难题,以直升机飞行数据为例对时间序列的反常检测问题展开了深入研究,并利用Iforest技术和PCA算法,给出了一个采用滑动窗口的时间序列异常检测方法,利用从滑动窗口采集信息的时间变化状态等数据信息,将序列异常检测问题转换为点异常检测问题;同时以auc评分为衡量标准,从带有时刻特殊标志的多个信息集上检验了检测效率的提高;在无标签的直升机飞行数据集上进行实验,验证了算法的有效性,并通过对比检测过程中不同特征变量的变化情况,从算法层面和现实层面上阐述了算法的可解释性。     

基于模糊网络和粒子群优化的油田指标预测

针对油田开发指标预测问题,提出一种模糊神经网络模型,该模型包括输入层、模糊化层、规则层和输出层。模糊化层采用高斯隶属函数,规则层每个节点对应一条模糊逻辑规则。网络可调参数为模糊集参数和输出层权值。提出了基于改进量子粒子群优化的网络训练方法。以油田开发指标中含水率预测为例,结果表明该方法是有效的可行的。     

基于模糊理论的行人异常动作检测

为在智能监控系统中自动识别行人的异常动作,提出简化的人体关节模型图。根据行人躯干和四肢轮廓角度的变化,设计用于模糊化的函数式。提出利用躯干和四肢的模糊隶属度通过计算来得到整个人异常度的一种基于模糊理论异常行为判别的算法。在系统实现中,提出利用质心轨迹和模糊判别的联合方法来甄别行人是否异常的方法。模糊判别可实现在视频监控范围内对行人行为的主动分析,从而能够对行人异常的动作做出识别并进行报警处理。通过实验证明该方法具有较高的识别率。     

A high-order fuzzy time series forecasting model for internet stock trading

Recently, many fuzzy time series models have already been used to solve nonlinear and complexity issues. However, first-order fuzzy time series models have proven to be insufficient for solving these problems. For this reason, many researchers proposed high-order fuzzy time series models and focused on three main issues: fuzzification, fuzzy logical relationships, and defuzzification. This paper presents a novel high-order fuzzy time series model which overcomes the drawback mentioned above. First, it uses entropy-based partitioning to more accurately define the linguistic intervals in the fuzzification procedure. Second, it applies an artificial neural network to compute the complicated fuzzy logical relationships. Third, it uses the adaptive expectation model to adjust the forecasting during the defuzzification procedure. To evaluate the proposed model, we used datasets from both the Taiwanese stock index from 2000 to 2003 and from the student enrollment records of the University of Alabama. The results of our study show that the proposed model is able to obtain an accurate forecast without encountering conventional fuzzy time series issues.     

基于神经-模糊控制系统的移动机器人动态路径规划

针对机器人在未知、复杂环境下从源到目标之间,避开各种类型的障碍的问题,设计了系统的神经-模糊控制算法进行动态路径规划：设计了合理的模糊推理体系,实现输入模糊化、模糊推理规则库、输出去模糊化控制;根据规则库设计神经网络结构,简化网络结构和参数;采用QPSO算法训练网络;状态变量的存储和管理策略,解决了“U”型障碍物内的死循环路径问题。实验结果表明,在以上算法的控制下,机器人能够朝着目标,规划产生合理的路径,不会陷入死循环。     

Effects of increasing fuzziness on analytic hierarchy process for spatial multicriteria decision analysis

Multicriteria decision analysis (MCDA) involves techniques which relatively recently have received great increase in interest for their capabilities of solving spatial decision problems. One of the most frequently used techniques of MCDA is Analytic Hierarchy Process (AHP). In the AHP, decision-makers make pairwise comparisons between different criteria to obtain values of their relative importance. The AHP initially only dealt with crisp numbers or exact values in the pairwise comparisons, but later it has been modified and adapted to also consider fuzzy values. It is necessary to empirically validate the ability of the fuzzified AHP for solving spatial problems. Further, the effects of different levels of fuzzification on the method have to be studied. In the context of a hypothetical GIS-based decision-making problem of locating a dam in Costa Rica using real-world data, this paper illustrates and compares the effects of increasing levels of uncertainty exemplified through different levels of fuzzification of the AHP. Practical comparison of the methods in this work, in accordance with the theoretical research, revealed that by increasing the level of uncertainty or fuzziness in the fuzzy AHP, differences between results of the conventional and fuzzy AHPs become more significant. These differences in the results of the methods may affect the final decisions in decision-making processes. This study concludes that the AHP is sensitive to the level of fuzzification and decision-makers should be aware of this sensitivity while using the fuzzy AHP. Furthermore, the methodology described may serve as a guideline on how to perform a sensitivity analysis in spatial MCDA. Depending on the character of criteria weights, i.e. the degree of fuzzification, and its impact on the results of a selected decision rule (e.g. AHP), the results from a fuzzy analysis may be used to produce sensitivity estimates for crisp AHP MCDA methods.     

Multi-objective optimal design of fuzzy controller for structural vibration control using Hedge-algebras approach

In this paper, the problem of multi-objective optimal design of hedge-algebras-based fuzzy controller (HAC) for structural vibration control with actuator saturation is presented. The main advantages of HAC are: (i) inherent order relationships among linguistic values of each linguistic variable are always ensured; (ii) instead of using any fuzzy sets, linguistic values of linguistic variables are determined by an isomorphism mapping called semantically quantifying mapping (SQM) based on a few fuzziness parameters of each linguistic variable and hence, the process of fuzzy inference is very simple due to SQM values occurring in the fuzzy rule base and (iii) when optimizing HAC, only a few design variables which are above fuzziness parameters are needed. As a case study, a HAC and optimal HACs (opHACs) based on multi-objective optimization view point have been designed to active control of a benchmark structure with active bracing system subjected to earthquake excitation. Control performance of controllers is also discussed in order to shown advantages of the proposed method.     

基于模糊理论的pH值控制系统设计及应用

本文通过阐述模糊PID控制器精确量的模糊化、规则库的建立以及产生模糊推理,结合锌冶炼沉铁工艺过程pH调节出现的问题,提出了在西门子控制系统基础上应用SCL语言建立模糊PID控制器。用模糊控制理论将pH值的偏差和pH值的偏差变化作为输入变量,以输出增量作为输出语言变量,实践表明,通过该方法建立的模糊控制器具有很强的鲁棒性和可靠性。     

Color image enhancement by fuzzy intensification

A Gaussian membership function to model image information in spatial domain has been proposed in this paper. We introduce a new contrast intensification operator, which involves a parameter t for enhancement of color images. By minimizing the fuzzy entropy of the image information, the parameter t is calculated globally. A visible improvement in the image quality for human contrast perception is observed, also demonstrated here by the reduction in ‘index of fuzziness’ and ‘entropy’ of the output image.     

Clustering large data with uncertainty

A new algorithm is designed for handling fuzziness while mining large data. A new novel cost function weighted by fuzzy membership, is proposed in the framework of CLARANS. A new scalable approximation to the maximum number of neighbors, explored at each node, is developed; thus reducing the computational time for large data while eliminating the need for user-defined (heuristic) parameters in the existing equation. The goodness of the generated clusters is evaluated in terms of Xie–Beni validity index. Results demonstrate the superiority of the proposed algorithm, over both synthetic and real data sets, in terms of goodness of clustering. It is interesting to note that our algorithm always converges to the globally best values at the optimal number of partitions. Moreover compared to existing fuzzy algorithms, FCLARANS without scanning the whole dataset, searching small number of neighbors, is able to handle the uncertainty due to overlapping nature of the various partitions. This is the main motivation of fuzzification of the algorithm CLARANS.     

基于批量模糊学习矢量量化的模糊系统辨识

提出一种基于批量模糊学习矢量量化的模糊系统辨识方法．首先通过优化方法自动调整模糊指数，使所得到的模糊规则前件隶属度函数与聚类规则得到的隶属度函数相比具有较好的可解释性；然后针对模糊系统可解释性与精度之间的困境问题，为保证参数的可理解性．利用带约束的非线性优化方法调整后件参数．并用调整参数的界评估因优化造成参数恶化的程度．仿真实验表明，利用该方法得到的模糊系统模型具有较高的透明度，满足合理的精度．     

基于模糊回归的质量屋决策模型

基于带有对称三角形模糊系数的模糊回归及模糊规划理论，提出关联函数及自 相关函数的数学模型，并在系统考虑资源约束影响的基础上，分别建立了基于质量屋的产品 规划精确模型及模糊模型．仿真研究表明，这些模型适合于各种工程设计问题，尤其是在不 确定的、模糊的条件下，能够有效地确定关联函数及自相关函数，帮助开发人员优化顾客需 求的满意水平，在资源约束下使产品的顾客满意度最大．