Linguistic harmonic mean operators and their applications to group decision making

Harmonic mean is the reciprocal of arithmetic mean of the reciprocal, which is a conservative average to be used to provide for aggregation lying between max and min operators. In this paper, we develop some new aggregation operators such as linguistic harmonic mean (LHM) operator, linguistic weighted harmonic mean (LWHM) operator, linguistic ordered weighted harmonic mean operator, and linguistic hybrid harmonic mean (LHHM) operator, which can be utilized to aggregate preference information taking the form of linguistic variables and then study some desirable properties of the operators. Based on the LHM and the LHHM operators, we propose a practical method for group decision making with linguistic preference relations and also give an illustrative example. Furthermore, based on the LWHM and LHHM operators, we develop a multiple attribute decision making applied in the group decision making to improving advanced manufacturing technology selection process.     

Neuro fuzzy model for adaptive filtering of oscillatory signals

In this paper we have developed a neuro fuzzy model for adaptive filtering of oscillatory signals embedded with white noise. Such type of fuzzy adaptive filters are constructed from a set of fuzzy IF-THEN rules, which change adaptively to minimise the cost function until a desired information is available. Here we have used a generalised cost function for better convergence of the error. This algorithm is simulated on a digital signal processor in order to track the signal and to filter out the disturbances present in the signal at a particular instant of time. The system presented here, can measure both types of information like numerical as well as linguistic.     

Expression of uncertainty in fuzzy scales based measurements

Fuzzy scales were introduced as a transition between weak scales and strong scales. Preceding studies on fuzzy scales considered only ideal exact measurement without any consideration of uncertainty. The goal of this paper is to present a general approach for the management of uncertainty within the context of fuzzy scale based measurements. After a short reminder on fuzzy scales, a method to define a probability density function or a possibility function on indications given by a fuzzy scale based measurement is exposed. Finally, a method based on the evidence theory is applied to build simultaneously a probability density function and an associated possibility function.     

Nonlinear Fuzzy Linguistics for the Determination of Nitrogen Monoxide,Nitrogen Dioxide,and Sulfur Dioxide by Molecular Absorption Spectroscopy

The nonlinearity and high dimensionality of spectra data affect the precision and the complexity of molecular absorption spectroscopy models. This article proposes a nonlinear fuzzy linguistic method for spectral quantitative analysis. A nonlinear fuzzy linguistic rule is presented. In the rule antecedent, a set operation was used to express the input variables by the fuzzy linguistic terms. A flexible polynomial equation of the input variables was the rule consequent. The fuzzy linguistic terms, the membership functions, and the nonlinear linguistic rules were initialized automatically by Gaussian kernel fuzzy clustering analysis, and the related parameters of nonlinear fuzzy linguistic rules were tuned by the iterative optimization for minimizing the root-mean square error. The principal components of the absorption measurements were extracted as input variables to reduce the complexity of the model. Experimental measurements employed a spectral dataset of flue gas for quantitative determination of the components that included sulfur dioxide, nitric oxide, and nitrogen dioxide. The experimental results verify the effectiveness of the theoretical approach.     

基于模糊集理论的医学CR图像增强

数字化X光影像可以划分为目标区和背景区两部分,进行医疗诊断的信息主要集中在目标区,因此在图像进行处理时应合理地区分两部分并采用不同的方法进行处理.本文引入模糊集的概念来描述目标区和背景区,并测定了隶属度函数,建立了基于模糊集理论的图像处理模型,给出了具体实现方法.处理后的图像增强了目标区图象的视觉效果,使医学信息得到了更好的表达,进而提高诊断的准确性.     

一种基于Sugeno模糊模型的测量数据处理方法

本文提出一种基于Sugeno模糊模型的、针对测量时间受限条件下测量数据处理的模糊决策方法。该方法利用概率理论和模糊逻辑,分析处理多个中间测量结果中存在的不确定因素,并对这些结果进行有效融合。在动态称重系统中的应用结果表明,该方法使系统测量精度有所提高。     

A rough set enhanced fuzzy approach to quality function deployment

Quality function deployment (QFD) provides a systematic methodology to assist companies in developing quality products that are able to satisfy customer needs. The house of quality (HOQ), as the first phase of QFD, plays the most important role in product development. Frequently, fuzzy numbers are used to quantify the vagueness of linguistic terms so as to facilitate subjective assessments in the HOQ. However, the issue concerning how to determine the boundary intervals of fuzzy numbers remains unresolved. This work proposes a novel approach based on rough set theory, and introduces two concepts called rough number and rough boundary interval to address this issue. A comparative case study presented in this work shows that the proposed approach has significant advantages compared to the prevailing fuzzy number based method in processing subjective linguistic assessments in QFD.     

不确定偏好信息下质量管理的含糊群体决策

为实现质量管理的定量决策和信息集结,将质量管理决策问题归结为偏好信息不确定下群体决策问题.建立了质量管理多属性群体决策模型,分析了决策中语言偏好信息和权重偏好信息的不确定性.采用双重模糊的含糊集表达决策语言的不确定性,同时将模糊集的基本运算引入到含糊集中,定义了四种处理权重偏好信息的决策函数,分别实现了质量管理决策量化结果的集结处理.比较了这些决策函数在权重偏好信息不同的情况下的集结行为,通过案例计算验证了这些决策函数对信息集结的不同效果和应用场合.     

多传感器数据融合新算法及在诊断学中的应用

着眼于复杂装备系统诊断实践中多传感器数据融合需求,提出了一种基于模糊贴近度的数据融合新算法,通过计算各个传感器测量值与估计值之间的模糊格贴近度,确定它们在测量中的权重,从而实现多传感器诊断参数数据融合.结合该算法原理,还设计了它的诊断数据融合应用软件.应用实例表明该算法体现了稳定性强、可靠性高的传感器在数据融合中的"优越性",运算过程简洁、快速,运算方法可行、有效,便于发动机诊断中实现时环境测量与数据处理.     

Nonparametric time series modelling for industrial prognostics and health management

Prognostics and health management (PHM) methods aim at detecting the degradation, diagnosing the faults and predicting the time at which a system or a component will no longer perform its desired function. PHM is based on access to a model of a system or a component using one or combination of physical or data-driven models. In physical-based models, one has to gather a lot of knowledge about the desired system and then build an analytical model of the system function of the degradation mechanism that is used as a reference during system operation. On the other hand, data-driven models are based on the exploitation of symptoms or indicators of degradations using statistical or artificial intelligence methods on the monitored system once it is operational and learn the normal behaviour. Trend extraction is one of the methods used to extract important information contained in the sensory signals, which can be used for data-driven models. However, extraction of such information from the collected data in a practical working environment is always a great challenge as sensory signals are usually multidimensional and obscured by noise. Also, the extracted trends should represent the nominal behaviour of the system as well as the health status evolution. This paper presents a method for nonparametric trend modelling from multidimensional sensory data so as to use such trends in machinery health prognostics. The goal of this work is to develop a method that can extract features representing the nominal behaviour of the monitored component, and from these features, smooth trends are extracted to represent the critical component’s health evolution over the time. The proposed method starts by multidimensional feature extraction from machinery sensory signals. Then, unsupervised feature selection on the features’ domain is applied without making any assumptions concerning the number of the extracted features. The selected features can be used to represent the nominal behaviour of the system and hence detect any deviation. Then, empirical mode decomposition algorithm is applied on the projected features with the purpose of following the evolution of data in a compact representation over time. Finally, ridge regression is applied to the extracted trend for modelling and can be used later for the remaining useful life prediction. The method is demonstrated on accelerated degradation data set of bearings acquired from PRONOSTIA experimental platform and another data set downloaded from NASA repository where it is shown to be able to extract signal trends.     

多传感器数据融合新算法及在诊断学中的应用

着眼于复杂装备系统诊断实践中多传感器数据融合需求,提出了一种基于模糊贴近度的数据融合新算法,通过计算各个传感器测量值与估计值之间的模糊格贴近度,确定它们在测量中的权重,从而实现多传感器诊断参数数据融合.结合该算法原理,还设计了它的诊断数据融合应用软件.应用实例表明:该算法体现了稳定性强、可靠性高的传感器在数据融合中的"优越性",运算过程简洁、快速,运算方法可行、有效,便于发动机诊断中实现时环境测量与数据处理.     

Evaluating the flexibility in a manufacturing system using fuzzy multi-attribute group decision-making with multi-granularity linguistic information

Evaluation of flexibility in a manufacturing system development in operations management is important to determine the competitiveness of manufacturing system, and is being increasing discussed in the literature on manufacturing system. This paper presents a fuzzy group decision-making model with different linguistic term sets (multi-granularity linguistic term sets) for evaluating manufacturing flexibility development, where the performance rating of manufacturing systems under flexibility metrics and the importance grade of all flexibility dimensions are assessed in linguistic terms represented by trapezoidal fuzzy numbers. The linguistic term sets chosen by decision-makers will have more or less terms. This paper proposes a procedure to assess the degree of manufacturing flexibility in a fuzzy environment by a fuzzy fusion method of linguistic information. While evaluating the degree of manufacturing flexibility, one may find the need for improving manufacturing flexibility, and determine the dimensions of manufacturing flexibility as the best direction to improvement. Example using a case of leading Taiwan firm in the bicycle industry is used to illustrate the computational process of the proposed method.     

基于粗糙集模糊控制的微孔钻削在线监测

提出了一种基于粗糙集模糊控制的微孔钻削在线监测的方法。克服了当模糊系统输入维数高时,系统模糊规则过多,计算过于复杂的缺点。在MATLAB环境下,应用构造好的模糊系统对主轴电机三相电流信号进行实时数据处理,获取隐含微细钻头磨损状态的信息值,对微孔钻削过程进行在线监测实验,结果表明,适当选择监测阈值,可以有效避免微细钻头的折断。     

The role of fuzzy scales in measurement theory

The introduction of the representational theory of measurement by Stevens initiated a new way to understand what measurement is and was followed by an intense scientific activity. Ludwik Finkelstein mainly contributed to this activity through several synthetic surveys and his formalisation of this theory includes a generalisation of the representation of measurement values to non-numerical sets. The role of group theory in the measurement theory was suggested by Stevens in his seminal paper. Such a role was explored by Narens and Luce for the ordered scales. The studied groups are homomorph to groups acting on real numbers, and other possible scales remain unexplored. For example, the metrical scales, introduced by Coombs, are built on distances and do not fit the classic classification of scale. Initially devoted to psychophysical measurement, metrical scales now appear in various fields, such as colour measurement or software measurement and need to be studied in more detail. The purpose of this paper is to revisit the group-based classification of scales and to show how such a classification includes metrical scales and more specifically fuzzy scales.     

基于云模型定性推理的液压弯辊板形控制

云模型将概率论的随机性和模糊集合论的模糊性相融合,能很好地解决不确定性问题,基于此设计了一种二维液压弯辊云模型控制器,其控制策略不需要被控对象的数学模型,只需通过语言原子和云模型将人用语言定性表达的经验和逻辑判断转换到语言控制规则器中,就能实现板形实时在线控制。以某公司1220mm液压弯辊板形控制系统为仿真对象进行仿真实验,结果表明,该控制器简易、快速、控制性能良好、鲁棒性强,较常规模糊控制具有更好的控制品质。     

基于视觉的柔性结构振动测量及其控制

针对柔性结构振动测试及控制问题,提出采用机器视觉测量结构振动并进行反馈控制的方法。为保证闭环控制系统的实时性,需要解决视觉图像处理数据量大、处理耗时长的问题。采取运动跟踪方法选择图像感兴趣区域(re-gion of interest,简称ROI)以减少数据处理量,并采用快速中值滤波算法提高图像处理速度。为了提高处理精度、保证处理结果的准确性,试验比较了3种微分边缘检测算子,采用Roberts算子和极值法提取图像中柔性结构末端边缘信息,并利用Hough直线变换方法进行修正。基于末端中心振动的信息反馈进行柔性梁结构振动控制理论分析和试验研究。结果表明,所采用的图像处理方法满足精度和实时性的要求,并验证了基于视觉反馈振动控制的可行性。     

基于聚类动态LS-SVM的L-赖氨酸发酵过程软测量方法

针对生化反应过程中软测量模型存在的模型失效问题,提出了一种基于模糊C均值聚类(FCM)和动态LS-SVM的混合建模方法.首先,采用FCM算法将训练集分成具有不同聚类中心的子集,然后对每一类分别采用LS-SVM进行训练并建立子模型.对于带有新信息的样本数据首先计算其对每一类的模糊隶属度函数,然后用隶属度最大的一类所对应的子模型进行动态学习,并更新子模型.将所提出的软测量建模方法用于对L-赖氨酸发酵过程关键生物量参数的预测,实验结果表明所提出的建模方法可以有效地增强软测量模型适应工况变化的能力,提高其预测精度.     

Selective noise filtration of image signals using wavelet transform

This paper examines the technique of denoising and signal extraction using wavelet transform scale space decomposition. The noisy signal is decomposed into multiple scales by the dyadic wavelet transform. At a given position, the level of correlation is used to deduce the presence or absence of significant feature of signals or images, which is then passed through the filter. By comparing the correlation information of the data at that scale with those at larger scales, noise is preferentially removed from the data. In the wavelet transform domain, the desired features are identified and retained because they are strongly correlated across scales compared to noise. This denoising algorithm can be used to reduce noise to a high degree of accuracy, while preserving most of the important features of the signal. In this paper, this technique of scale space filtering is applied to sample signals and images. Representative results are presented which shows that considerable noise content in signals and images can be reduced while preserving the value of the desired signal.     

Pattern classification by a neurofuzzy network: application to vibration monitoring

An innovative neurofuzzy network is proposed herein for pattern classification applications, specifically for vibration monitoring. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to automatically deduce fuzzy if-then rules based on a hybrid supervised learning scheme. The neurofuzzy classifier proposed is equipped with a one-pass, on-line, and incremental learning algorithm. This network can be considered a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. The classification performance of the proposed neurofuzzy network is validated on the Fisher's Iris data, which is a well-known benchmark data set. For the generalization capability, the neurofuzzy network can achieve 97.33% correct classification. In addition, to demonstrate the efficiency and effectiveness of the proposed neurofuzzy paradigm, numerical simulations have been performed using the Westland data set. The Westland data set consists of vibration data collected from a US Navy CH-46E helicopter test stand. Using a simple fast Fourier transform technique for feature extraction, the proposed neurofuzzy network has shown promising results. Using various torque levels for training and testing, the network achieved 100% correct classification.