Two-Sided Stable Matching Decision-Making Method Considering Matching Intention under a Hesitant Fuzzy Environment

In this paper,a stable two-sided matching(TSM)method considering the matching intention of agents under a hesitant fuzzy environment is proposed.The method uses a hesitant fuzzy element(HFE)as its basis.First,the HFE preference matrix is transformed into the normalized HFE preference matrix.On this basis,the distance and the projection of the normalized HFEs on positive and negative ideal solutions are calculated.Then,the normalized HFEs are transformed into agent satisfactions.Considering the stable matching constraints,a multiobjective programming model with the objective of maximizing the satisfactions of two-sided agents is constructed.Based on the agent satisfaction matrix,the matching intention matrix of two-sided agents is built.According to the agent satisfaction matrix and matching intention matrix,the comprehensive satisfaction matrix is set up.Furthermore,the multiobjective programming model based on satisfactions is transformed into a multiobjective programming model based on comprehensive satisfactions.Using the G-S algorithm,the multiobjective programming model based on comprehensive satisfactions is solved,and then the best TSM scheme is obtained.Finally,a terminal distribution example is used to verify the feasibility and effectiveness of the proposed method.

Two-Sided Matching Decision Making with Multi-Attribute Probabilistic Hesitant Fuzzy Sets

In previous research on two-sided matching(TSM)decision,agents’preferences were often given in the form of exact values of ordinal numbers and linguistic phrase term sets.Nowdays,the matching agent cannot perform the exact evaluation in the TSM situations due to the great fuzziness of human thought and the complexity of reality.Probability hesitant fuzzy sets,however,have grown in popularity due to their advantages in communicating complex information.Therefore,this paper develops a TSM decision-making approach with multi-attribute probability hesitant fuzzy sets and unknown attribute weight information.The agent attribute weight vector should be obtained by using the maximum deviation method and Hamming distance.The probabilistic hesitancy fuzzy information matrix of each agent is then arranged to determine the comprehensive evaluation of two matching agent sets.The agent satisfaction degree is calculated using the technique for order preference by similarity to ideal solution(TOPSIS).Additionally,the multi-object programming technique is used to establish a TSM method with the objective of maximizing the agent satisfaction of two-sided agents,and the matching schemes are then established by solving the built model.The study concludes by providing a real-world supply-demand scenario to illustrate the effectiveness of the proposed method.The proposed method is more flexible than prior research since it expresses evaluation information using probability hesitating fuzzy sets and can be used in scenarios when attribute weight information is unclear.

A novel vehicle navigation map matching algorithm based on fuzzy logic and its application

A new real-time map matching algorithm based on fuzzy logic is proposed. 3 main factors affecting the reliability of map matching, including the distance between the vehicle location and the matching road segment, the angle between the vehicle direction and the road segment direction and the road connectivity are discussed. Fuzzy rules for the distance, angle and connectivity are presented to calculate the matching reliability. 2 indicators for estimating the matching reliability are then derived, one is the lower limit of the reliability, and the other is the limit error of the difference between the maximal value and the second-maximal value of the reliability. A real-time map-matching system based on fuzzy logic is therefore developed. Using the real data of global positioning system(GIS) based navigation and geographic information system(GPS) based road map, the method is verified and the (results) prove the effectiveness of the proposed method.

New delay-dependent robust stability and stabilization for uncertain T-S fuzzy time-delay systems under imperfect premise matching

To alleviate the conservativeness of the stability criterion for Takagi-Sugeno （T-S） fuzzy time-delay systems, a new delay-dependent stability criterion was proposed by introducing a new augmented Lyapunov function with an additional triple-integral term, which was firstly u3ed to derive the stability criterion for T-S fuzzy time-delay systems. By the same approach, the robust stability issue for fuzzy time-delay systems with uncertain parameters was also considered. On the other hand, in order to enhance the design flexibility, a new design approach for uncertain fuzzy time-delay systems under imperfect premise matching was also proposed, which allows the fuzzy controller to employ different membership functions from the fuzzy time-delay model. By the numerical examples, the proposed stability conditions are less conservative in the sense of getting larger allowable time-delay and obtaining smaller feedback control gains. For instance, when the allowable time-delay increases from 7.3 s to 12 s for an uncertain T-S fuzzy control system with time-delay, the norm of the feedback gains decreases from （34.299 2, 38.560 3） to （10.073 3, 11.349 0）, respectively. Meanwhile, the effectiveness of the proposed design method was illustrated by the last example with the robustly stable curves of system state under the initial condition of x（0） = [3 -1].

Stability analysis and controller design of T-S fuzzy systems with time-delay under imperfect premise matching

The issue of the stability and controller design of Takagi-Sugeno（T-S） fuzzy control systems with time-delay is investigated under imperfect premise matching when the T-S fuzzy time-delay model and fuzzy controller do not share the same membership functions.A new stability criterion which contains the information of membership functions is derived.The new stability criterion is less conservative,and enhances the design flexibility.Two numerical examples are presented to illustrate the conservativeness and effectiveness of the proposed method.

Error Searching System with Keyword Extraction and Keyword Fuzzy Matching

This paper has proposed an error searching method to search the solutions of errors that occurred in the unified commanding platform mix-deployed software (UCPMD). Because those errors belong to different stages or may be happened in different services, applications, IP ports, system software, or different versions of software, and those errors are also can be classified into different types. It is necessary to locate accurate reason that cause an error as well as find out its solution. The proposed error searching system applies Chinese keyword extraction and Chinese fuzzy matching between keywords, which considers the processed keywords as the index to find out the solutions of errors. Besides, the error searching system had made correspondence among errors, reasons, and solutions, and put them to different categories in terms of their characteristics, such that it is easy to manage, search, and use. Among others, we have added specialized thesaurus as the index of keywords, which enriches and completes the searching results. Because of the proposed error searching system evolves keyword extraction and keyword fuzzy matching technologies;it is more accurate to find out user-interested solutions.

Image matching navigation based on fuzzy information

In conventional image matching methods, the image matching process is mostly based on image statistic information. One aspect neglected by all these methods is that there is much fuzzy information contained in these images. A new fuzzy matching algorithm based on fuzzy similarity for navigation is presented in this paper. Because the fuzzy theory is of the ability of making good description of the fuzzy information contained in images, the image matching method based on fuzzy similarity would look forward to producing good performance results. Experimental results using matching algorithm based on fuzzy information also demonstrate its reliability and practicability.

AN H_∞ FUZZY TRACKING CONTROL SCHEME FOR AFFINE COUPLED SPATIO-TEMPORAL CHAOS

Due to the interactions among coupled spatio-temporal subsystems and the constant bias term of affine chaos, it is difficult to achieve tracking control for the affine coupled spatiotemporal chaos. However, every subsystem of the affine coupled spatio-temporal chaos can be approximated by a set of fuzzy models; every fuzzy model represents a linearized model of the subsystem corresponding to the operating point of the controlled system. Because the consequent parts of the fuzzy models have a constant bias term, it is very difficult to achieve tracking control for the affine system. Based on these fuzzy models, considering the affine constant bias term, an H∞ fuzzy tracking control scheme is proposed. A linear matrix inequality is employed to represent the feedback controller, and parameters of the controller are achieved by convex optimization techniques. The tracking control for the affine coupled spatio-temporal chaos is achieved, and the stability of the system is also guaranteed. The tracking performances are testified by simulation examples.

GPS probe map matching algorithm based on spatial data model

To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm based on the Oracle spatial data model is proposed. The algorithm uses the Oracle road network data model to analyze the spatial relationships between massive GPS positioning points and freeway networks, builds an N-shortest path algorithm to find reasonable candidate routes between GPS positioning points efficiently, and uses the fuzzy logic inference system to determine the final matched traveling route. According to the implementation with field data from Los Angeles, the computation speed of the algorithm is about 135 GPS positioning points per second and the accuracy is 98.9%. The results demonstrate the effectiveness and accuracy of the proposed algorithm for mapping massive GPS positioning data onto freeway networks with complex geometric characteristics.

Data Recovery Based on Intelligent Pattern Matching

To solve the problem of data recovery on free disk sectors, an approach of data recovering based on intelligent pattern matching is proposed in this paper. Different from the methods based on the file directory, this approach utilizes the consistency among the data on the disk. A feature pattern library is established based on different types of fries according to the internal constructions of text. Data on sectors will be classified automatically by data clustering and evaluating. When the conflict happens on data classification, the digestion will be initiated by adopting context pattern. Based on this approach, the paper achieved the data recovery system aiming at pattern matching of txt, word and PDF fries. Raw and formatting recovery tests proved that the system works well.

Assessing Risk in Chemical Plant with Pattern Matching

This paper discusses potential application of fuzzy set theory,more specifically, pattern matching, in assessing risk in chemicalplants. Risk factors have been evaluated using linguisticrepresentations of the quantity of the hazardous substance involved,its frequency of interaction with the environment, severity of itsimpact and the uncertainty involved in its detection in advance. Foreach linguistic value there is a corresponding membership functionranging over a universe of discourse. The risk scenario created by ahazard/hazardous situation having highest degree of featural value istaken as the known pattern.

Application of nonlinear color matching model to four-color ink-jet printing

Through discussing the color matching technology and its application in printing industry the conventional approaches commonly used in color matching, and the difficulties in color matching, a nonlinear color matching model based on two step learning is established by finding a linear model by learning pure color data first and then a nonlinear modification model by learning mixed color data. Nonlinear multiple regression is used to fit the parameters of the modification model. Nonlinear modification function is discovered by BACON system by learning mixture data. Experiment results indicate that nonlinear color conversion by two step learning can further improve the accuracy when it is used for straightforward conversion from RGB to CMYK. An improved separation model based on GCR concept is proposed to solve the problem of gray balance and it can be used for three to four color conversion as well. The method proposed has better learning ability and faster printing speed than other historical approaches when it is applied to four color ink jet printing.

A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images.Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental conditions.Here,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions.Accordingly,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images.The divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDR algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize endmembers.The endmember with the minimum error is chosen as the final endmember in each specific category.The proposed method is simple and automatically considers endmember variability in hyperspectral images.The efficiency of the proposed method is evaluated using two real hyperspectral datasets.The average spectral angle and abundance angle are used to analyze the performance measures.

基于Pythagorean模糊环境下MABAC方法的网络货运平台车货匹配研究

针对网络货运平台车货匹配过程中存在的平台间的价格战、卡车司机间的抢单、平台推荐匹配方案不合理问题,在Pythagorean模糊环境下采用MABAC(多属性边界近似区域比较)决策方法,在考虑货主特殊要求的同时对平台接收到的货物订单进行匹配。在考虑车主、货主、平台三方利益的前提下建立车货匹配评价指标体系;采用Pythagorean模糊数表示专家评价语言,采用反正切Pythagorean模糊熵计算指标权重和专家权重;依据MABAC方法对备选方案进行择优排序。

燃料电池汽车动力系统匹配及能量管理

为了分析燃料电池汽车动力系统参数匹配设计以及能量管理方案对整车性能的影响,以某款燃料电池汽车为研究对象,对其主要动力系统部件进行了参数匹配.以提升整车经济性能为目标,提出了功率跟随式能量管理策略和基于模糊控制的能量管理策略,并采用滑动平均滤波算法对模糊控制策略输出量进行优化.基于AVL-Cruise和Simulink建立了联合仿真平台,搭建整车及能量管理系统模型,验证了整车动力性能,对比分析了3种控制策略下的整车经济性能.结果表明:经过优化后的能量管理策略燃料电池输出功率曲线更加平滑,且一直处于高效率输出区间内,基于燃料电池优先保护原则,经过优化后的能量管理策略性能最佳.

垂直大气湍流影响下的天气雷达回波信号优化检测方法

垂直大气湍流导致回波信号在大气中扩散,使回波信号质量较差,极易被噪声所淹没,因此,提出垂直大气湍流影响下的天气雷达回波信号优化检测方法。通过分析垂直大气湍流对天气雷达回波信号传输特性的影响,匹配滤波优化算法,改善垂直大气湍流影响导致质量下降的回波信号,利用模糊逻辑方法提取回波特征参数并构建对应隶属函数,设定阈值,获取天气雷达回波信号检测结果。实验结果表明:该方法可有效检测不同仰角的天气雷达回波信号,回波信号波形均匀且波动幅度小,检测效果较好。

毕达哥拉斯模糊环境下基于TODIM的双边匹配决策方法

本文考虑决策者在实际问题中存有的多种决策心理,针对毕达哥拉斯模糊偏好下考虑双边主体损失规避和参照依赖心理行为的双边匹配问题提出一种决策方法。对于双边主体给出的毕达哥拉斯模糊偏好信息,依据TODIM(Tomada de Decisao Interativa Multicritério)思想获得双边主体相较于另一边匹配主体的总体优势度,进而构建双边主体的满意度矩阵;而后,在考虑双边主体一对一的数量匹配约束下,以实现双边主体满意度最大化为决策目标,建立多目标双边匹配决策模型;最后,通过线性加权法进一步将其转化为单目标双边匹配模型,通过模型求解获得最优双边匹配方案;一个实际供应链管理系统软件的交易匹配算例验证本方法的可行性和有效性。

燃料电池重卡动力系统参数匹配与能量管理策略仿真分析

为提高燃料电池重卡综合性能,首先确定了其动力系统构型方案,依据整车动力性与经济性要求对驱动电机、燃料电池以及动力电池进行部件选型和参数匹配,利用AVL Cruise软件搭建了整车物理模型。制定了一种基于模糊控制方法的能量管理策略,并在MATLAB/Simulink环境下建立模糊控制能量管理策略模型。通过MATLAB DLL方式实现了能量管理策略模型与整车物理模型的联合仿真。仿真结果表明所提出的整车动力系统设计方案和能量管理策略能够满足整车动力性并有效地提高了整车的燃料经济性。

多重不确定性条件下长三角地区多行业CCUS源汇匹配模型研究与应用

随着“双碳”目标的提出,长三角地区面临一定的减排压力。合理布局规划碳捕集利用与封存(CCUS)项目对保障长三角地区能源安全,平稳实现区域“双碳”目标具有重要意义。CCUS技术的发展面临诸多不确定性因素,对CCUS未来时空部署形成巨大挑战。然而,现有CCUS源汇匹配研究多采用混合整数线性规划等方法,未充分考虑排放源、技术和政策等因素变化对CCUS减排潜力、宏观布局及成本效益的影响,导致规划结果与实际情况相比可能存在一定的偏差,给CCUS大规模部署带来较大挑战。为解决复杂不确定条件下CCUS源汇匹配问题,通过模糊可能性、随机机会约束和区间线性规划等方法,构建基于区间模糊机会约束规划方法的CCUS源汇匹配模型,应用在中国长三角地区CCUS源汇匹配的实际问题中,探究风险管理和决策支持方案。研究发现:若考虑不确定性,长三角地区CCUS项目总成本将高于确定性模型的评估结果;在违约风险为40%的情况下,长三角CCUS项目源汇匹配总成本最低,平准化减排成本低于30美元/t的排放源占筛选出排放源总数的38.5%,累计厂级碳捕集潜力56.9亿t。因此,将多重不确定性纳入模型综合考虑,探究不确定性条件下的CCUS源汇匹配部署方案,在提高项目经济性、可行性等方面具有重要的实际意义。

An Approach to Unsupervised Character Classification Based on Similarity Measure in Fuzzy Model

This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the repre...