一种模糊Rough决策方法

利用模糊集理论和粗糙集理论在处理不确定性和不精确性问题方面侧重点的差异性，构造一种组合决策模型。该模型从问题领域内的部分不精确信息出发利用模糊聚类方法构造一个决策信息系统，利用粗糙集理论关于决策规则的约简方法从决策信息系统中提取（挖掘）决策规则，使之适用于问题的整个领域。     

基于区间数度量的区间值模糊集合的相似度、模糊度和包含度的关系研究

区间值模糊集合的相似度、模糊度和包含度及其关系研究是区间值模糊集合的一个研究热点。考虑到区间值模糊集合所表示信息的丰富性,本文使用区间数而非实数来刻画区间值模糊集合的包含度,首先给出基于区间数度量的区间值模糊集合的包含度的公理化定义,然后通过五个定理详细研究了基于公理化定义的区间值模糊集合的相似度、包含度和模糊度之间的相互转换,最后,给出了若干计算公式来计算基于区间数度量的区间值模糊集合的相似度、模糊度和包含度。这些结论,一方面丰富了区间值模糊集合的信息测度(相似度、模糊度和包含度)的内容,另一方面也为区间值模糊集合的近似推理、决策分析、模式识别等领域的应用提供了新方法和新理论。     

模糊相似度推理算法及其构造的模糊系统逼近性能的分析

本文首先将相似度理论进一步推广,给出了模糊相似度的公理化定义;然后给出了基于模糊相似度的推理算法,证明了该算法是规则再现的;进一步地,对基于模糊相似度推理算法构造的模糊系统的逼近性能进行了理论分析,给出了该系统具有插值性的充要条件,并给出了满足该条件的两个模糊相似度.最后,证明了当满足一定条件时,该系统具有二阶逼近精度.     

用粗集-模糊神经网络评定空袭目标威胁程度

针对模糊神经网络运算过程中,当模糊规则较多时,网络学习速度慢,方法实时性差的缺点,本文提出采用粗糙集理论对该模型进行优化,该方法利用粗集数据分析方法,通过知识约简从数据中推理逻辑规则,并用约简后规则集作为模糊神经网络的规则将输入映射到输出的子空间上：在这个子空间上用改进的BP算法训练进行逼近。实验结果表明：通过粗集数据挖掘后提取的规则,不仅规则数目减少,且规则是不完全规则,减少了网络输入维数和各层神经元的个数,提高了网络运算速度,满足了系统实时性要求。     

基于模糊神经网络的设备故障预测研究

对模糊神经网络技术进行了研究，提出了预测分析的模糊神经网络模型；建立了故障指标评定方法，利用预测算法运用参数历史故障指标对参数指标进行趋势预测，预测得到的参数指标可以根据专家诊断系统判据进行诊断，对未来设备的健康状况进行可信度较高的评估。经仿真结果验证，该算法预测精度较高，预测结果可信.     

一种变量是梯形模糊数的两层多随从线性规划模型及其算法

针对具有递阶特征的多层管理系统,本文建立了一种变量为梯形模糊数的两层多随处线性规划模型.利用模糊结构元理论,通过模糊数的结构元加权序,将梯形模糊数的排序转化为单调有界函数的排序,从而证明了该模型的最优解等价于两层多随处线性规划模型的最优解;进而提出了求解该模型的有效算法.最后,通过两个数值算例验证了该方法的可行性.     

用粗集-模糊神经网络评定空袭目标威胁程度

针对模糊神经网络运算过程中,当模糊规则较多时,网络学习速度慢,方法实时性差的缺点,本文提出采用粗糙集理论对该模型进行优化,该方法利用粗集数据分析方法,通过知识约简从数据中推理逻辑规则,并用约简后规则集作为模糊神经网络的规则将输入映射到输出的子空间上:在这个子空间上用改进的BP算法训练进行逼近.实验结果表明:通过粗集数据挖掘后提取的规则,不仅规则数目减少,且规则是不完全规则,减少了网络输入维数和各层神经元的个数,提高了网络运算速度,满足了系统实时性要求.     

一类变量为梯形模糊数的线性规划

针对变量为梯形模糊数的模糊线性规划问题,利用结构元方法定义了一种模糊数的排序准则,讨论了如何将变量是梯形模糊数的线性规划去模糊化,即将含有变量为梯形模糊数的模糊线性规划转化为经典模糊线性规划.同时,证明了该模型的最优解等价于经典的线性规划的最优解,再利用单纯形法求出最优解.并设计了求解该类模型的算法.通过算例验证了该方法的可行性和算法的有效性,从而为变量模糊的广义模糊线性规划问题的研究提供了新的方法.     

基于模糊K-均值算法的模糊分类器设计

基于模糊K-均值算法的模糊分类器,就是把目前比较常用的模糊K-均值算法的聚类方法,再一次与模糊分类规则提取相结合而得到的一种分类器。它是一种很有效的模糊分类器,训练样本能正确的分类。在这种方法中,首先用模糊K-均值算法按剖分和覆盖的原则把训练样本分成群,并且每一群的中心和半径都被计算出来。然后,设计一个用模糊规则来表示分类的模糊系统。这样就有效地构建了一个能对训练样本比较准确分类的模糊分类器。用这种方法设计的分类器不需要预定义参数、训练时间较短、方法简单     

基于模糊边缘检测的复杂晶界的重建

提出了一种基于模糊边缘检测提取复杂晶界的实用方法。首先通过预处理调整图像对比度、消除晶粒内部灰度差和划痕，进而消除伪边界；再引入模糊理论来判别和跟踪边界，模糊边缘检测算法的特点是不需要确定门限值，具有很强的自适应性；最后进行细化去枝、修补等得到单像素宽度的晶界。实验结果显示，该方法在有效抑制伪边界的同时提取出了比较理想的晶界，为晶粒度分析创造了良好的条件。     

Fuzzy rules extraction using a hybrid of base‐n‐number‐coded GA and SVD‐QR

Abstract

A hybrid of a base‐n‐number‐coded genetic algorithm (base‐n‐number‐coded GA) and an SVD‐QR is proposed to construct a fuzzy system directly from some gathered input‐output data of the identified system. Each individual in the base‐n‐number‐coded GA is applied to determine the fuzzy sets in each input variable. However, the grid‐type fuzzy partition by the fuzzy sets associated with each input variable may generate some redundant fuzzy subspaces. Therefore, an SVD‐QR method is applied to remove the redundant fuzzy subspaces to efficiently describe the behavior of the identified system so that the premise part of the fuzzy system is determined. Then, the recursive least‐squares method is used to determine the consequent part of the fuzzy system. Subsequently, a fitness function is defined such that it can guide the search procedure to select an appropriate fuzzy system that not only maintains a good performance but also has relevant fuzzy rules. Finally, two nonlinear system identification problems are used to illustrate the efficiency of the proposed method.     

Generating interpretable fuzzy rules for adaptive job dispatching

Adaptive scheduling is an approach that selects and applies the most suitable strategy considering the current state of the system. The performance of an adaptive scheduling system relies on the effectiveness of the mapping knowledge between system states and the best rules in the states. This study proposes a new fuzzy adaptive scheduling method and an automated knowledge acquisition method to acquire and continuously update the required knowledge. In this method, the criteria for scheduling priority are selected to correspond to the performance measures of interest. The decisions are made by rules that reflect those criteria with appropriate weights that are determined according to the system states. A situated rule base for this mapping is built by an automated knowledge acquisition method based on system simulation. Distributed fuzzy sets are used for evaluating the criteria and recognizing the system states. The combined method is distinctive in its similarity to the way human schedulers accumulate and adjust their expertise: qualitatively establishing meaningful criteria and quantitatively optimizing the use of them. As a result, the developed rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop effective and robust rules.     

一种基于软计算的转子故障诊断方法

提出了一种基于软计算的转子故障诊断方法。该方法充分利用软计算中的模糊集合理论,人工神经网 络,粗糙集理论和遗传算法等计算方法优势,弥补它们相互的不足,进行故障诊断。首先利用粗糙集理论对样本数据进 行初步规则获取,并计算规则的依赖度和条件覆盖度,然后根据这些规则进行网络设计,其中,网络隐层节点的数目等于 规则的数目,初始网络权重由规则的依赖度和条件覆盖度确定,最后用遗传算法对模糊神经网络参数进行优化。使用该 网络对转子类常见故障进行诊断。实验表明,和一般模糊神经网络相比,这种基于软计算的诊断方法具有训练时间短、 诊断准确率高的特点。     

一种基于粗糙集的模糊信息融合方法及应用

将粗糙集理论和模糊逻辑技术结合起来，提出了一种基于粗糙集数据处理的模糊信息融合方法。运用粗糙集的基本理论和简约计算方法，从大量原始数据中发现精简的、概略化的规则，结合模糊逻辑推理建立一致粗糙模糊模型，并提出了对模型进行扩充与完备化的概念。脉动真空灭菌温度控制过程的仿真试验研究结果表明了所提方法的有效性和可行性。     

Assessment of Structural Damage Condition Based on Fuzzy Pattern Recognition

This paper presents a new method of damage condition assessment that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness that are statistically non-describable. In this method, healthy observations are used to construct a fuzzy set representing sound performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed by using the state similarity matrix. Thus, an optimal group fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. The optimal group of damage fuzzy sets is used to classify a set of observations at any unknown state of damage using the principles of fuzzy pattern recognition based on an approximate principle. This method can be embedded into the system of Structural Health Monitoring (SHM) to give advice about structural maintenance and life prediction. Finally, a case study, which comes from Reference [9] for damage pattern recognition is presented and discussed. The compared result illustrates our method is more effective and general, so it is very practical in engineering.     

随机模糊信息系统的知识约简

利用模糊逻辑中的R-型蕴涵算子定义随机模糊信息系统对象集上的模糊等价关系,进而实现对随机模糊信息系统知识的近似表示。讨论随机模糊下近似、上近似集的模糊概率与模糊信任测度、模糊似然测度之间的关系。给出基于模糊信任测度和模糊似然测度的随机模糊信息系统知识约简的方法。     

基于随机模糊集的粗糙集模型

当知识库中的知识模块既是模糊的又是随机得到的,我们定义了基于随机模糊集的粗糙集模型。给出了随机模糊粗糙集的性质。讨论了利用模糊集的下近似和上近似定义的模糊测度和概率模糊测度的关系。     

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.     

A RFID-based recursive process mining system for quality assurance in the garment industry

With the increasing concern about product quality, attention has shifted to the monitoring of production processes to be assured of good quality. Achieving good quality is a challenging task in the garment industry due to the great complexity of garment products. This paper presents an intelligent system, using fuzzy association rule mining with a recursive process mining algorithm, to find the relationships between production process parameters and product quality. The goal is to derive a set of decision rules for fuzzy logic that will determine the quantitative values of the process parameters. Learnt process parameters used in production form new inputs of the initial step of the mining algorithm so that new sets of rules can be obtained recursively. Radio frequency identification technology is deployed to increase the efficiency of the system. With the recursive characteristics of the system, process parameters can be continually refined for the purpose of achieving quality assurance. A case study is described in which the system is applied in a garment manufacturing company. After a six-month pilot run of the system, the numbers of critical defects, major defects and minor defects were reduced by 7, 20 and 24%, respectively while production time and rework cost improved by 26 and 30%, respectively. Results demonstrate the practical viability of the system to provide decision support for garment manufacturers who may not be able to determine the appropriate process settings for achieving the desired product quality.     

Nondeterministic decision tables in process control

In this paper we define nondeterministic decision tables to describe process control rules specified imprecisely. An example of such a control rule is ‘if temperature ishigh and pressure islow then open valveslightly”. The definition of nondeterministic decision tables is based on fuzzy sets and associated logic. We show how nondeterministic decision tables are interpreted and specified actions executed based on measured values of independent control variables. When nondeterministic decision tables are formulated based on rules given by experts it is necessary to determine whether they have any redundant rules, missing rules or contradictory rules. We define these terms for nondeterministic decision tables and show how such logical errors can be detected in certain cases. Grant of a fellowship to N R Garud by the Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore for doing this research is gratefully acknowledged.