区间条件下基于GRA和TOPSIS的辐射源威胁评估

将辐射源威胁评估作为多属性决策问题进行处理时,由于作战环境的复杂性,侦察方无法获取敌方辐射源的完整信息,导致各个辐射源目标的属性取值通常不是确定数值,具有一定的不确定性.针对上述问题,引入区间数理论和灰色关联分析对传统逼近理想解排序法进行拓展,提出一种基于灰色关联分析和逼近理想解排序的区间多属性决策方法用于解决不确定条件下的辐射源威胁评估问题.该方法构建基于区间数的欧氏距离和基于区间数的灰色关联度,通过对二者进行结合构建关于战场态势的决策信息系统,并通过构造新的综合贴近度实现对辐射源威胁程度的定量评价.实验仿真验证了所提方法能够有效解决实际作战中不确定条件下的辐射源威胁排序问题,有助于侦察方对于战场态势的进一步掌握.     

机载雷达告警系统中威胁等级判定方法研究

在机载雷达告警系统中,辐射源威胁等级判定对自动实施有效的干扰功率分配和提高作战飞机的生存能力至关重要。针对现在作战飞机所面临的多目标作战情况,利用模糊数学建立了威胁指标的隶属度模型,通过层次分析法和熵值法获得了各威胁指标的主观和客观权重,用综合赋权法融合了主客观权重,最后根据机载雷达告警系统的综合性能要求,建立了辐射源威胁等级判定函数,实现了对辐射源威胁等级的判定。通过实例应用说明了该方法能够有效地判定辐射源的威胁等级。     

针对预警机的主客观权重相结合的TOPSIS法多目标排序

针对传统威胁评估模型主要适用于战斗机的现状,根据预警机作战特点及敌机战术方法,提出了针对预警机的威胁评估模型;传统TOPSIS法确定指标权重存在人为因素偏大的不足,根据GD-AHP法考虑多位专家意见并结合信息熵得到综合权重,进而对目标更客观地进行威胁评估并排序;算例分析表明,该方法合理、有效。     

基于AHP的威胁评估与排序模型研究

层次分析法(AnalyticHierarchyProcess,简称AHP)把一个复杂问题表示为有序的递阶层次结构,通过定性判断和定量计算,将经验判断给予量化,对决策方案进行排序,是一种定性分析与定量分析相结合的决策分析方法,适用于多准则决策问题。通过仿真实验用AHP根据威胁程度的大小对敌空袭目标进行排序,介绍了威胁评估和排序的求解过程,以此提供敌空袭目标对我保卫目标或区域所构成的威胁等级。该方法有效地解决了对敌空中目标威胁评估与排序问题。     

改进TOPSIS的多时刻融合直觉模糊威胁评估

针对防空作战中的目标威胁评估问题,提出一种新的多时刻融合直觉模糊数排序模型.首先,根据目标威胁属性的主、客观权重得到综合权重;然后,通过逼近理想解排序法衡量直觉模糊数信息量的大小,利用直觉模糊熵表征直觉模糊数信息的可靠性,并结合决策者的风险偏好构建基于信息量和不确定性的直觉模糊数排序模型,得出单时刻的目标威胁排序;最后,利用泊松分布逆形式构建时间序列权重,从而融合多个时刻的决策信息,得出最终的目标威胁排序.仿真结果表明,所提出的算法综合了多时刻的决策信息,并可根据决策者的风险偏好进行调整,灵活性强、可靠性高.     

数据结构的内排序方法研究

排序在计算机程序设计中非常重要,各种排序方法各有其优缺点,适用场合也不同。本文从多个方面对各种内排序方法进行全面的比较和分析,最后给出综合结论。     

基于层次分析法(AHP)的空中目标威胁度估计

为了对空中目标的威胁度进行科学评估和排序,以便于指挥员正确作出决策和作战指令,提出基于AHP的空中目标威胁估计的方法和具体步骤。首先分析影响空中目标威胁度的具体指标因素,同时构建各个因素的威胁因子和评判函数,然后利用一种改进型的层次分析算法对空中目标的威胁度进行全面综合的评估排序。实验案例结果表明,该评估方法的应用解决...     

多属性目标决策的分类融合威胁排序的模型

协同空战中,目标威胁等级的判定为武器资源的有效配置提供了重要依据,是现代作战指挥决策系统的核心内容.目前该问题的难点在于,如何解决多属性目标量化属性和非量化属性的混合比较问题.运用目标多属性理论探索对空中目标的威胁排序问题,提出了一种基于被保护对象相互关系鼍化的空中目标威胁评估和排序的方法,方法对威胁冈素进行了详细分类,并分析了它们之间的关系,在此基础上融入了我方目标任务属性的比较关系,同时对一些模糊属性进行了量化处理.最后通过示例介绍了威胁评估和排序的求解过程.该方法有效地解决了目标威胁评估与排序问题,是可行和有效的.     

基于灰色群体决策的对地多目标攻击研究

针对地多目标攻击时,目标威胁度评估可以为武器资源的有效配置提供依据。由于威胁评估和多属性决策的特点,为准确识别目标,运用灰色群体决策理论对目标威胁度评估进行研究。建立基于灰色群体决策理论的对地多目标攻击决策模型,使对地攻击决策问题转化为对目标威胁度的求解,通过求解模型即可获得目标威胁度的排序。然后根据协同优先权对多机对地多目标攻击进行目标分配和攻击排序。最后通过仿真表明模型的合理性和有效性,从而为对地多目标攻击提供一种有效的决策方法。     

生产调度算法的研究

本文发展了文献⑴的排序算法并提出了加班算法，适用于小批量，多品种生产的调度。     

模糊匹配方法及证据理论在辐射源识别中的应用

辐射源识别是面向区域电子战的一个重要组成部分，关系到平台识别以及后续的态势分析，但传统的辐射源识别方法并不适用于日益复杂的电磁环境，本文阐述了如何将模糊匹配方法与证据理论相结合进行辐射源识别，在说明模糊匹配方法与证据理论基本概念的基础上，较系统地论述了识别算法，在最后做了仿真实验，取得了较满意的效果。     

Exact fuzzy optimal solution of fully fuzzy linear programming problems with unrestricted fuzzy variables

Kumar et al. (Appl. Math. Model. 35:817?C823, 2011) pointed out that there is no method in literature to find the exact fuzzy optimal solution of fully fuzzy linear programming (FFLP) problems and proposed a new method to find the fuzzy optimal solution of FFLP problems with equality constraints having non-negative fuzzy variables and unrestricted fuzzy coefficients. There may exist several FFLP problems with equality constraints in which no restriction can be applied on all or some of the fuzzy variables but due to the limitation of the existing method these types of problems can not be solved by using the existing method. In this paper a new method is proposed to find the exact fuzzy optimal solution of FFLP problems with equality constraints having non-negative fuzzy coefficients and unrestricted fuzzy variables. The proposed method can also be used to solve the FFLP problems with equality constraints having non-negative fuzzy variables and unrestricted fuzzy coefficients. To show the advantage of the proposed method over existing method the results of some FFLP problems with equality constraints, obtained by using the existing and proposed method, are compared. Also, to show the application of proposed method a real life problem is solved by using the proposed method.     

Optimal route selection based on Monte Carlo method and adaptive amoeba algorithm under uncertain environment

The fuzzy optimal path under uncertainty is one of the basic network optimization problems. Considering the uncertain environment, many fuzzy numbers are used to represent the edge weights, such as interval number and triangular fuzzy number. Then, these fuzzy numbers are converted to real numbers directly. This converting makes the optimal path the shortest path selection problem. However, much information of uncertainty get lost when converting fuzzy numbers to real numbers. In order to ensure all the origan data complete, in this paper, a fuzzy optimal path solving model based on the Monte Carlo method and adaptive amoeba algorithm is proposed. In Monte Carlo process, a random number which belongs to the fuzzy number is generated. Then, Physarum polycephalum algorithm is used to solve the shortest path every time and record the result. After many times calculation, many shortest paths have been found and recorded. At last, by analysing the characters of all the results, the optimal path can be selected. Several numerical examples are given to illustrate the effectiveness of the proposed method, the results show that the proposed method can deal with the fuzzy optimal path problems effectively.     

辐射源识别的模糊联想分类器设计

The fuzzy associative classifier is investigated in this paper. The design methods of the fuzzy associative classifier with genetic algorithm for training are presented. This method trains the weight and back terms to obtain classification rules automatically. Radar radiant points are classified by using of this algorithm, and the simulation results show that the method has higher identification precision than available fuzzy classifiers.     

A NEW METHOD FOR HANDLING MULTICRITERIA FUZZY DECISION-MAKING PROBLEMS

This paper presents a new method for handling multicriteria fuzzy decision-making problems, in which the characteristics of the alternatives are represented by interval-valued fuzzy sets, and some techniques are developed to calculate the degree of similarity between interval-valued fuzzy sets. The proposed method is more flexible than the one we presented earlier (Chen et al., 1989) because it allows the criteria values of the alternatives to be represented by real intervals rather than crisp real values between zero and one.     

/spl epsiv/-insensitive fuzzy c-regression models: introduction to /spl epsiv/-insensitive fuzzy modeling

This paper introduces a new epsilon-insensitive fuzzy c-regression models (epsilonFCRM), that can be used in fuzzy modeling. To fit these regression models to real data, a weighted epsilon-insensitive loss function is used. The proposed method make it possible to exclude an intrinsic inconsistency of fuzzy modeling, where crisp loss function (usually quadratic) is used to match real data and the fuzzy model. The epsilon-insensitive fuzzy modeling is based on human thinking and learning. This method allows easy control of generalization ability and outliers robustness. This approach leads to c simultaneous quadratic programming problems with bound constraints and one linear equality constraint. To solve this problem, computationally efficient numerical method, called incremental learning, is proposed. Finally, examples are given to demonstrate the validity of introduced approach to fuzzy modeling.     

A new fuzzy weighted average (FWA) method based on left and right scores: An application for determining a suitable location for a gas oil station

The selection of a facility location from alternative locations is a multiple criteria decision making (MCDM) problem including both quantitative and qualitative criteria. In many real-life cases, determining the exact values for MCDM problems, and especially for facility location selection problems, is difficult or impossible, so the values of alternatives with respect to the criteria or/and the values of criteria weights are considered as fuzzy values (fuzzy numbers) such that the conventional crisp approaches for solving facility location selection problems and other MCDM problems tend to be less effective for dealing with the imprecise or vagueness nature of the linguistic assessments. In such conditions, fuzzy MCDM methods are applied for facility location selection problem and other fuzzy MCDM problems. In this paper, we propose a new fuzzy weighted average (FWA) method based on left and right scores for fuzzy MCDM problems. Moreover, we apply the proposed method to a real application. As a result, we found that the proposed method is practical for facility location selection problems. Besides, it seems that the proposed FWA method is very accurate, flexible, simple, and easy to use when compared to other versions of the FWA method.     

Method for solving unbalanced fully fuzzy multi-objective solid minimal cost flow problems

In fuzzy single and multi-objective minimal cost flow (MCF) problems, it is assumed that there is only one conveyance which can be used for transporting the product. However, in real life problems, more than one conveyance is used for transporting the product. To the best of our knowledge untill now no method is proposed in the literature for solving such fuzzy single and multi-objective MCF problems in which more than one conveyance is used for transporting the product and all the parameters, as well as all the decision variables that are represented by fuzzy numbers. In this paper, these types of fuzzy multi-objective MCF problems are called fully fuzzy multi-objective solid minimal cost flow (SMCF) problems and a new method is proposed for solving these problems. The advantages of the proposed methods are also discussed.     

A new fuzzy linear assignment method for multi-attribute decision making with an application to spare parts inventory classification

In this paper, a novel fuzzy linear assignment method is developed for multi-attribute group decision making problems. Since uncertain nature of many decision problems, the proposed method incorporates various concepts from fuzzy set theory such as fuzzy arithmetic and aggregation, fuzzy ranking and fuzzy mathematical programming into a fuzzy concordance based group decision making process. Fuzziness in the group hierarchy and quantitative type criteria are also taken into account. In order to present the validity and practicality of the proposed method, it is applied to a real life multi-criteria spare part inventory classification problem. The case study has demonstrated that the proposed method is easy to apply and able to provide effective spare parts inventory classes under uncertain environments. In addition to the practical verification by the company experts, the proposed method is also compared with some of the commonly used fuzzy multi-attribute decision making methods from the literature. According to the comparison of the results, there is an association between classes of spare parts obtained by the proposed method and the benchmarked methods.     

Optimal design of radial basis function neural networks for fuzzy-rule extraction in high dimensional data

The design of an optimal radial basis function neural network (RBFNF) is not a straightforward procedure. In this paper we take advantage of the functional equivalence between RBFN and fuzzy inference systems to propose a novel efficient approach to RBFN design for fuzzy rule extraction. The method is based on advanced fuzzy clustering techniques. Solutions to practical problems are proposed. By combining these different solutions, a general methodology is derived. The efficiency of our method is demonstrated on challenging synthetic and real world data sets.