模糊多属性决策理论在军事通信系统效能评估中的应用

决策中定性指标的量化一般通过一个模糊量化标度来实现，没有考虑决策者评判时的置信程度。提出了一种基于置信度的定性指标的量化方法。把模糊多属性决策引入到复杂通信系统的效能评估中来，建立了基于模糊多属性决策的系统效能评估模型。利用指标值和权重值都为三角模糊数的模糊多属性决策方法实现了对某战区通信系统的效能评估。     

维数差异对模糊积分融合多FasART网络影响的研究

基于软决策的并行分布式融合系统能融合不同类型的特征,且最终决策结果与前期决策器输出的数值大小相关.当不同类型矢量的维数不等时,维数差异是否对最终融合决策有影响对设计融合算法和分类器的选择具有重要意义.本文分析了维数差异对模糊积分融合多FasART网络算法的影响原因,提出了解决办法,用遥感图像的光谱和纹理特征进行了融合实验,结果表明维数差异对该算法有影响.模糊积分融合多FasART网络是一种典型的基于软决策的并行分布式融合决策系统的算法,因此,基于上述融合系统的算法存在维数差异问题.     

基于模糊积分控制遗传算法的军事决策仿真

针对军事决策系统中的任务指派问题,提出一种适用于军事决策的模糊积分控制遗传算法。首先对典型遗传算法的原理和不足进行分析;然后设计了一种优化的自调节模糊积分控制器,并将其与遗传算法相结合以便提高搜索和收敛的速度,提高解的质量;最后将提出的模糊积分控制遗传算法在军事决策系统中进行Matlab证了提出算法的可行性和正确性。仿真结果显示,相比标准遗传算法,模糊积分控制遗传算法具有更高的稳定性。     

基于模糊决策的工作流互操作研究

针对现有工作流管理系统对模糊信息处理的不足之处,分析了分布、异构工作流管理系统对决策最优工作流的需求,提出了一种基于模糊决策的择优工作流实现方法,研究了模糊决策法在不同工作流互操作中的实现.最后开发了一个原型工作流系统,实现了基于模糊决策法的不同工作流中的信息交互.     

基于群决策的多业务无线接入选择方法

在异构无线环境中,为了给运行了多个业务(群呼叫)的移动终端选择一个合适的无线接入网络,提出了一种基于群决策的接入选择方法。该方法引入效用函数对网络QoS及功耗进行评价;根据业务特点分配各属性的模糊权重,并采用业务优先权来反映各业务在群决策中的相对重要性;基于模糊理论,设计合理的模糊TOPSIS数据聚合模型,并在效用评价基础上实现了群决策。仿真结果表明,该方法能有效地实现多业务的无线接入选择,并且业务优先权在很大程度上影响了群决策结果。     

基于群决策的多业务无线接入选择方法

在异构无线环境中,为了给运行了多个业务（群呼叫）的移动终端选择一个合适的无线接入网络,提出了一种基于群决策的接入选择方法。该方法引入效用函数对网络QoS及功耗进行评价;根据业务特点分配各属性的模糊权重,并采用业务优先权来反映各业务在群决策中的相对重要性;基于模糊理论,设计合理的模糊TOPSIS数据聚合模型,并在效用评价基础上实现了群决策。仿真结果表明,该方法能有效地实现多业务的无线接入选择,并且业务优先权在很大程度上影响了群决策结果。     

基于模糊神经网络的声母识别

模板匹配法技术是汉语声母识别中较为成功的算法,但它的缺陷影响了其恢复错误、改善识别性能。神经网络(NN)和模糊系统的结合,保留了双方的优点,充分利用了模糊神经网良好的容错性能、计算性能、分类性能和决策性能。本文重点研究了两种基于模糊神经网的声母识别方案,通过对其结构、识别率和特点的分析,可看出模糊神经网的声母识别性能明显优于模板匹配法,是更适于语音识别的网络。     

高速公路模糊综合评价系统的研究与设计

文章基于语言变量的模糊决策方法，构建了某高速公路综合评价系统。在基于语言变量的模糊MCDM问题中。提出了一种新的语言模糊集截集决策方法，在一定的范围内，这种方法不仅能够决策出最佳方案，而且还能给出备选方案间的一个清晰的排序。     

基于多Agent偏好的决策信息融合

针对多Agent系统的群决策问题，本文提出了一种利用多Agent群体偏好进行决策融合的方法，使用群决策方法生成多Agent群体偏好，然后根据模糊排序策略进行决策信息的融合，群决策方法与模糊排序这两种方法的混合运用有效地降低了决策时间，提高了决策的精度。经过实验模拟，证明了这种方法的简单性和有效性。     

基于修正因子的模糊PID控制在装甲车辆稳瞄系统中的应用

简要分析了影响稳瞄系统稳定精度的因素以及系统的性能需求。针对经典PID控制算法在该系统上存在的缺点和不足,设计了一种基于量化因子和决策因子自修正的模糊PID控制器,利用子模糊系统自适应调整量化因子和决策因子。实验结果表明,该方法克服了经典控制算法存在的不足,能有效地隔离载体扰动,系统的稳定精度和动态性能都得到了显著提高。     

A fuzzy logic and game theory based adaptive approach for securing opportunistic networks against black hole attacks

Opportunistic networks (OppNets) are networks that can be used in situations of sparse network connectivity. Strategies for implementing secure transmission of messages in OppNets offer complex and distinctive problems that are wholly different from traditional networks using TCP/IP protocols for data transmission. In this paper, a security protocol named FuzzyPT is proposed which combats black hole attacks in OppNets. The defense mechanism is based on the information extracted from messages in the buffer, potential threat (PT) messages, and fuzzy logic. Fuzziness aids the system in being adaptive by modeling the character of nodes as either benign or malicious. It provides the network with the ability to judge the character of nodes based on relationships between different parameters instead of basing this decision on fixed conditions. FuzzyPT is observed to reduce erroneous decision‐making and decrease the number of false positives and false negatives in the system. An evolutionary game theoretic model is applied, which analyzes the decision‐making ability of relays in choosing varied strategies while forwarding messages. The evolutionary stable state is computed for the protocol, which verifies the proposition that a majority of the relays will cooperate by forwarding messages. The efficiency of the protocol is judged on various grounds such as delivery probability, network overhead ratio, packets dropped, and transmission latency. It is observed that the proposed game theoretic protocol outperforms an existing acknowledge and evolutionary game theory–based security protocol in terms of all the aforementioned performance characteristics.     

用于实时识别时域信号的神经网络统计模糊算法

本文讨论了一种基于统计训练和模糊判断的非监督神经网络算法，网络的权重是在已知样本的概率分布下得到的，输出单元兴奋状态及兴奋程度由被识别样本与确定权重的内积和被识别样本的模的隶属函数确定，网络的识别采用模糊聚类分析方法。最后给出了计算机模拟结果，证实了方法的正确性和可靠性。     

无线传感器网络模糊逻辑分簇路由协议

在无线传感器网络（Wireless Sensor Network,WSN）中,LEACH协议通过概率模型来选举簇头,由于没有考虑到传感器节点的分布情况和能量剩余等信息,可能会使得部分节点过早死亡.针对这一问题,提出基于模糊逻辑的分簇路由协议（DFLCP）.在预选簇头阶段,根据节点剩余能量等信息利用模糊逻辑计算出节点的竞争半径,使得簇头分布相对均匀;在簇头选举阶段,通过模糊逻辑确定节点成为簇头的概率.仿真结果表明：DFLCP协议可有效控制簇头节点的分布密度和簇的半径,均衡网络负载,延长节点平均生存时间.     

基于模糊逻辑的呼叫允许控制研究

无线频谱资源的缺乏以及用户的移动性使无线网络的服务质量的供给成为一个日益严峻的问题。为了满足服务质量的需要,该文提出蜂窝移动通信系统的一种模糊呼叫允许控制方案。它自动搜寻基站中保护信道数量的最优值,使资源得到合理的利用并保证服务质量的供给。对提出的模糊方案和一种动态信道预留方案进行了仿真比较,仿真结果表明模糊方案具有较强的鲁棒性,方案的呼叫阻止率、切换掉线率和信道利用率等性能参数都优于自适应方案。     

模糊神经网络在客户信用评级中的应用研究

应用模糊理论的解释能力及神经网络自组织、自学习、自适应等的优点组建模糊神经网络模型，用于商业银行企业客户的信用风险评级，在组织网络时采用结构替代型神经网络，很好地将模糊系统与神经网络融合到一起，并产生模糊规则，训练结果表明系统误差小。     

Nonlinear system modeling by competitive learning and adaptivefuzzy inference system

Modeling nonlinear systems by neural networks and fuzzy systems encounters problems such as the conflict between overfitting and good generalization and low reliability, which requires a great number of fuzzy rules or neural nodes and uses very complicated learning algorithms. A new adaptive fuzzy inference system, combined with a learning algorithm, is proposed to cope with these problems. First, the algorithm partitions the input space into some local regions by competitive learning, then it determines the decision boundaries for local input regions, and finally, based on the decision boundaries, it learns the fuzzy rule for each local region by recursive least squares (RLS). In the learning algorithm, the key role of the decision boundaries is highly emphasized. To demonstrate the validity of the proposed learning approach and the new adaptive fuzzy inference system, four examples are studied by the proposed method and compared with the previous results     

Call admission control in wideband CDMA cellular networks by using fuzzy logic

In this paper, a novel call admission control (CAC) scheme using fuzzy logic is proposed for the reverse link transmission in wideband code division multiple access (CDMA) cellular communications. The fuzzy CAC scheme first estimates the effective bandwidths of the call request from a mobile station (MS) and its mobility information, then makes a decision to accept or reject the connection request based on the estimation and system resource availability. Numerical results are given to demonstrate the effectiveness of the proposed fuzzy CAC scheme in terms of new call blocking probability/handoff call dropping probability, outage probability, and resource utilization.     

改善用户体验的垂直切换算法

针对由超密集异构无线网络的超高动态性而引起掉话率不断增长的问题,并考虑到以往基于模糊逻辑相关垂直切换算法的较大时间开销,该文提出一种改善用户体验的垂直切换算法。首先利用5G核心访问和移动管理功能发现终端附近的所有候选网络,同时,利用自组织网络技术的环境感知能力,随时监测网络的运行状况,主动维护网络之间的邻居关系表。然后,引入动态模糊神经网络(DFNN)算法来执行切换判决,将获取到的网络参数数据作为该系统的输入,动态生成对垂直切换有效的规则库,经学习之后,计算得到输出判决值,从而为终端选择最佳接入网络。最后,仿真结果表明,该算法能够明显改善垂直切换过程中的掉话情况,降低切换失败概率。同时,与其他同类算法的时间消耗相比,该算法能够维持在一个较低的水平。     

智能电气火灾预警系统研究

在分析现有电气火灾监控系统的不足基础上,设计了一种基于神经网络和模糊推理判决的智能电气火灾预测模型,通过传感器采集电气线路运行参数信号,送入经过训练学习的神经网络模型处理后,将输出的概率作为模糊推理判决输入,最后得到电气火灾发生概率。实验表明,该方法很好地反映了电气线路的电流、漏电、弧光、温度及现场电磁参量之间的非线性关系,能够较为准确地计算发生电气火灾的概率,能够有效提高电气火灾预测精度。     

Self-evolving neural networks for rule-based data processing

Two training algorithms for self-evolving neural networks are discussed for rule-based data analysis. Efficient classification is achieved with a fewer number of automatically added clusters, and application data is analyzed by interpreting the trained neural network as a fuzzy rule-based system. The learning vector quantization algorithm has been modified, acquiring the self-evolvement character in the prototype neuron layer based on sub-Bayesian decision making. The number of required prototypes representing fuzzy rules is automatically determined by the application data set. This method, compared with others, shows better classification results for data sets with high noise or overlapping classification boundaries. The classifying radial basis function networks are generalized into multiple shape basis function networks. The learning algorithm discussed is capable of adding new neurons representing self-evolving clusters of different shapes and sizes dynamically. This shows a clear reduction in number of neurons or the number of fuzzy rules generated, and the classification accuracy is increased significantly. This improvement is highly relevant in developing neural networks that are functionally equivalent to fuzzy classifiers since the transparency is strongly related to the compactness of the system