一种基于模糊推理的自组网成簇控制策略

研究了模糊控制技术的使用．对于每种度量，定义了相应的模糊隶属度函数用于预测一个更稳定的链路，实现了一个模糊推理规则库以产生每种链路的模糊代价，并提出一种基于移动预测机制的δ-度约束的分布式成簇策略．成簇问题可归结为寻找一个极大独立集问题，给出了算法及其正确性证明．仿真结果表明，该方法的稳定性优于HD算法和LCC算法．     

基于模糊AHP的CDMA网络资源利用率的评价研究

为了确保CDMA网络资源的合理利用,提出一种基于模糊AHP的CDMA无线网络资源利用效率的评价方法.采用yaahp层次分析软件通过对CDMA无线网络资源指标选择建立层次分析模型,确定了各指标的组合权重;利用模糊综合评判（FCA）方法建立模糊评价模型,计算CDMA网络资源利用的综合评价并做出决策.实际案例表明,该方法通过将层次分析法和模糊综合评价方法相结合,有效的对定性指标和定量指标进行综合权衡分析,评价模型科学、合理地反映出实际评价结果,为CDMA无线网络资源及时调配提供合理依据.     

面向机器人装配的DFA模糊评价方法

本文论述了面向机器人装配生产方式的DFA评价技术 研究的特点,并提出了一种面向机器人装配的产品可装配性模糊综合评价方法．     

装备制造企业供应商综合评价系统

针对国内装备制造业特点,分析了全球供应链条件下的供应商评价体系.分析了国内装备制造产品的主要特征,如产品装配过程复杂、供应链管理复杂、产品构型管理复杂等.提出了一种新的供应商评价方法.设计了一种基于三角模糊数的评价指标体系构建方法.应用层次分析得到评价指标权重,采用灰色关联度对企业供应商综合能力进行评估;以某装备制造企业供应商管理为研究背景,建立了供应商评价模型,通过应用实例分析说明了本方法的实用性     

电子商务环境下信用评级的一种新方法

为提高电子商务环境下信用评价水平，构建了一套完整的电子商务环境下信用指标体系，提出了一种基于拉开档次机理的模糊综合评判法，该方法改进了传统的拉开档次法．将其用在评价指标权重的确定上，采用模糊综合评判法对各指标值进行融合．结果表明，基于拉开档次机理的模糊综合评判法能够放大不同电子商务主体的信用差距，达到鼓励电子商务环境下基于信用机制的竞争的效果．该方法适用于需要放大被评价对象间差异的情形．     

基于多层次模糊综合评价的选煤厂分选效果评价

为了反映选煤厂产品满足指标要求的情况,提出了多层次模糊综合评价方法。以某选煤厂7个月的生产数据为研究对象,选取重介分选产品指标、粗煤泥分选产品指标和浮选产品指标构成模糊关系矩阵,运用专家评分法建立权重向量,通过多层次模糊综合评价方法对该选煤厂7个月的分选效果进行评价。结果表明,1月和7月的分选效果为"好",2—5月的分选效果为"较好",6月的分选效果为"一般",此结论与现场分选效果相符。     

基于灰色关联分析和D-S证据理论的区间直觉模糊决策方法

针对方案的指标值为区间直觉模糊数的决策问题,提出了一种基于灰色关联分析和D-S证据理论的决策方法. 定义了区间记分函数和区间数点算子, 并通过其将区间直觉模糊数转化为记分函数;利用记分函数以及灰色关联方法确定各指标的不确信度, 进而构建出不同指标下各方案的Mass函数, 通过D-S合成法则进行信息融合,确定最优方案.最后,通过算例表明, 本文提出的方法可得到满意结果并显著降低决策的不确定性.     

基于Web的快速原型工艺选择系统

提出一种快速成型(RP)工艺优选方法．该方法综合运用了专家系统和模糊综合评判,通过专家系统初步确定RP候选方案,采用模糊综合评判选择最适合的RP工艺;运用层次分析法,建立了RP工艺优选的多因素、多层次综合评价模型,确定了准则层各个评价因素的权重,并使用Java技术开发了这个工艺选择系统．通过一个典型应用案例展示了该系统的使用方法及其应用效果．结果表明,通过该方法确定的RP工艺更加准确和有效,能够更好地满足用户的需求．     

一种基于中介真值程度度量的模糊综合评价方法

鉴于现有的综合评价方法大多为刚性的评价,不适用于模糊因素评价,提出了一种基于中介真值程度度量的模糊综合评价方法,对效益型指标、成本型指标和区间型指标3种情况下的真值程度度量过程和参数取值方法进行了设计;并基于真值程度度量结果构造了中介模糊评判矩阵来进行综合评价。 为处理模糊信息创建了中介数学系统。相比传统模糊评价方法,基于中介真值程度度量的模糊综合评价方法更具有客观性,因此能更有效地处理模糊现象。与其他综合评价方法的对比实验表明,所提出的方法是有效的,并具有一定的优越性。     

船舶电力推进系统六相同步电机选型AHP FCE方法研究

根据船舶电力推进六相同步电机选犁方案的评价指标,针对多相电机评价指标的复杂性,对评价指标进行了一致化和无量纲化处理,提出了一种改进型隶属函数的确定方法,建立了基于层次分析法（AHP）的电机选型方案的模糊综合评判,实现了评价指标类型的无量纲化,并利用层次分析法确定了隶属函数与电机选型方案的权系数。通过实例验证了此方法的正确性和有效性,通过与传统隶属函数确定方法的比较,发现改进型隶属函数通用性强,更加易于分析、接受、理解和应用。     

An integrated form-feature-based design system for manufacturing

Much of the knowledge that is applied in or communicated between design and manufacturing activities is primarily shape based or shape indexed. Previous attempts to acquire and organize shape knowledge have been mostly concentrated on feature recognition from solid models, group technology (GT) coding schemes, and feature-based modeling. This paper presents the development of an efficient form-feature-based modeling system, and addresses the important issue of utilizing feature information for manufacturing, which has not been extensively discussed by previous work. In this paper we first present a Euler operator-based approach for efficient and effective form-feature encoding and manipulation in a feature-based design environment. Subsequently, a hybrid representation scheme called enhanced CSG tree of feature (ECTOF), which integrates feature model with solid model in a tree structure, is discussed. A feature interference resolution methodology to maintain the correct and consistent feature information in an ECTOF is also deliberated. Finally, we present a machinability-checking module, which employs global accessibility criteria to analyze a feature's machinability on a three-axis machining center. By developing feature interference resolving and machinability testing techniques and integrating with an efficient feature-based design system, this research makes the development of an integrated feature-based design and manufacturing system possible.     

On-line multi-stage sorting algorithm for agriculture products

This paper presents an on-line multi-stage sorting algorithm capable of adapting to different populations. The sorting algorithm selects on-line the most appropriate classifier and feature subsets for the incoming population. The sorting algorithm includes two levels, a low level for population detection and a high level for classifier selection which incorporates feature selection. Population detection is achieved by an on-line unsupervised clustering algorithm that analyzes product variability. The classifier selection uses n fuzzy kNN classifiers, each trained with different feature combinations that function as input to a fuzzy rule-based decision system. Re-training of the n fuzzy kNN classifiers occurs when the rule based system cannot assign an existing classifier with high confidence level. Classification results for synthetic and real world databases are presented.     

结合多特征和模糊偏好关系的高分辨率遥感影像分割

高空间分辨率(简称高分辨率)遥感影像除光谱特征外,还包含丰富的纹理特征,为了实现高分辨率遥感影像的高精度分割,提出结合多特征和模糊偏好关系的分割方法.首先,通过像素光谱测度定义多种统计特征,根据定义的各个特征提取特征影像并分别实现影像分割,利用其结果构建模糊决策矩阵;然后,基于像素定义特征间的模糊偏好关系矩阵,计算不同特征对最终分割决策的权重,并对模糊决策矩阵加权以突出优势特征,抑制劣势特征;最后,通过反模糊化决策矩阵得到最优影像分割结果.对合成影像和真实高分辨率遥感影像的分割结果进行定性和定量评价,结果表明,合成影像的分割总精度为99.8%,Kappa值为0.998,说明所提出的算法通过结合各特征的优势部分能够获得高精度的分割结果.     

并行工程下基于特征的零件可制造性及其评价方法研究*

结合并行工程的思想,对零件可制造性进行较详细的分析,提出并行工程环境下基于特征的可制造性分析方法,研究建立基于特征的可制造性零件定义模型及评价体系。阐述了基于特征的可制造性的评价方法,认为可制造性评价是一个多层次的决策问题,建立了基于制造约束规则的加工可行性定性评价和基于模糊综合评判的加工可行性定量评价两种评价方法,并给出了解法。利用上述研究方法,全面合理地对零件的可制造性进行评价和反馈,从而指导产品设计。最后以涡轮叶片榫头特征的可制造性分析为应用实例,说明了该方法的有效性和实用性。     

基于成对约束的动态加权半监督模糊核聚类

针对样本间的不均衡性,提出一种基于成对约束的动态加权半监督模糊核聚类算法。在传统模糊聚类算法中加入半监督学习机制,通过Mercer核将原数据空间映射到特征空间,为特征空间中的每个向量分配一个动态权值,由此得到新的目标函数,并结合一种简单的核参数选择方法实现数据分类。理论分析和实验结果表明,与模糊核聚类算法及成对约束的竞争群算法相比,该算法具有更好的聚类效果。     

Strong fuzzy c-means in medical image data analysis

This paper presents a robust fuzzy c-means (FCM) for an automatic effective segmentation of breast and brain magnetic resonance images (MRI). This paper obtains novel objective functions for proposed robust fuzzy c-means by replacing original Euclidean distance with properties of kernel function on feature space and using Tsallis entropy. By minimizing the proposed effective objective functions, this paper gets membership partition matrices and equations for successive prototypes. In order to reduce the computational complexity and running time, center initialization algorithm is introduced for initializing the initial cluster center. The initial experimental works have done on synthetic image and benchmark dataset to investigate the effectiveness of proposed, and then the proposed method has been implemented to differentiate the different region of real breast and brain magnetic resonance images. In order to identify the validity of proposed fuzzy c-means methods, segmentation accuracy is computed by using silhouette method. The experimental results show that the proposed method is more capable in segmentation of medical images than existed methods.     

A new approach to fuzzy modeling of nonlinear dynamic systems with noise: relevance vector learning mechanism

This paper presents a new fuzzy inference system for modeling of nonlinear dynamic systems based on input and output data with measurement noise. The proposed fuzzy system has a number of fuzzy rules and parameter values of membership functions which are automatically generated using the extended relevance vector machine (RVM). The RVM has a probabilistic Bayesian learning framework and has good generalization capability. The RVM consists of the sum of product of weight and kernel function which projects input space into high dimensional feature space. The structure of proposed fuzzy system is same as that of the Takagi-Sugeno fuzzy model. However, in the proposed method, the number of fuzzy rules can be reduced under the process of optimizing a marginal likelihood by adjusting parameter values of kernel functions using the gradient ascent method. After a fuzzy system is determined, coefficients in consequent part are found by the least square method. Examples illustrate effectiveness of the proposed new fuzzy inference system.     

离群模糊核聚类算法

一般说来,离群点是远离其他数据点的数据,但很可能包含着极其重要的信息.提出了一种新的离群模糊核聚类算法来发现样本集中的离群点.通过Mercer核把原来的数据空间映射到特征空间,并为特征空间的每个向量分配一个动态权值,在经典的FCM模糊聚类算法的基础上得到了一个特征空间内的全新的聚类目标函数,通过对目标函数的优化,最终得到了各个数据的权值,根据权值的大小标识出样本集中的离群点.仿真实验的结果表明了该离群模糊核聚类算法的可行性和有效性.     

Analytical structure and stability analysis of a fuzzy PID controller

Analytical structure for a fuzzy PID controller is introduced by employing two fuzzy sets for each of the three input variables and four fuzzy sets for the output variable. This structure is derived via left and right trapezoidal membership functions for inputs, trapezoidal membership functions for output, algebraic product triangular norm, bounded sum triangular co-norm, Mamdani minimum inference method, and center of sums (COS) defuzzification method. Conditions for bounded-input bounded-output (BIBO) stability are derived using the Small Gain Theorem. Finally, two numerical examples along with their simulation results are included to demonstrate the effectiveness of the simplest fuzzy PID controller.     

Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition

A fuzzy reasoning based expert system is developed for the recognition of welds in radiographic images. First, each object in a radiographic image is identified and described with a three-feature vector. Of interest are to distinguish welds from non-weld objects in order to extract welds for further processing, such as, flaws detection. To this end, a fuzzy reasoning method is proposed. The fuzzy rules are extracted from feature data one feature at a time based on a modified fuzzy c-means algorithm. The number of fuzzy terms with overlapping between adjacent terms and the shape of terms are optimized based on the mean squared error criterion. The total number of fuzzy rules is the product of the number of fuzzy terms for each feature. The performance of this optimal set of fuzzy rules is tested with unseen data in terms of accurate rate, false positive rate, and false negative rate. For comparison, selected sets of rules are extracted by varying the number of fuzzy terms for each feature and subsequently tested. The performance of the fuzzy expert system is also found to be better than that of multi-layer perceptron neural networks, if appropriately designed.