Information fusion in data association applications

There is a limitation to process data fusion by means of traditional deterministic or probabilistic data association algorithm of multisensor data fusion (MSDF). Those methods for data association do not adequately account for quantitative and qualitative information in an automated fashion. Fuzzy logic offers an enabling technology for automated quantitative and qualitative information in the data fusion process. We propose the fusion system architecture, called fuzzy gating approach, coordinating both quantitative and qualitative information which is realized using a fuzzy-based reasoning approach. This approach is composed of two stages in cascade. The first stage implements available quantitative information, namely target range, azimuth, and elevation angle, to form a subset of statistically likely target solutions via fuzzy validation. The second stage of the fuzzy similarity utilizes available qualitative information, namely infrared image area and brightness, to form another subset of returns. Finally the fuzzy gating approach was tested in a dense clutter environment. Results of test show that performance of fuzzy gating approach is superior to JPDA based on a Bayesian approach. Moreover, adding qualitative information to the tracking algorithm can improve tracker performance effectively.     

故障诊断的信息融合方法

对基于信息融合的故障诊断方法进行综述．首先简要阐述信息融合的基本概念以及信息融合与故障诊断的关系;然后介绍贝叶斯定理融合故障诊断、模糊融合故障诊断、证据理论融合故障诊断、神经网络融合故障诊断和集成信息融合故障诊断方法的诊断原理与步骤。并分析其特点和局限性;最后给出了信息融合故障诊断研究的若干发展方向．     

一种新的模糊神经元网络学习方法

提出了一种模糊神经元网络的学习算法即利用多 层多层模糊IF/THEN规则表达专家知识的神经网络学习方法，在以此构造的基于多源信息融合的分类系统中，采用了多层模糊IF/THEN规则进行分类。为了处理模糊语言值，提出了一种能够控制模糊输入矢量的神经网络体系结构。该方法能够对非线性实间隔矢量和模糊矢量进行分类，工程实验表明，此学习算法是切实可行的。     

On the divergence of information filter for multi sensors fusion

The paper deals with the divergence of information filter in multi sensor target tracking problem using bearing only measurements. Information filter has a number of advantages in terms of computational requirements over Kalman filter for target tracking applications. Compared to Kalman filter it also has the advantage that one can start estimation even without an initial estimate. But this filter is seen to diverge after tracking for a short period of time, even when the target is moving at a constant velocity. A technique to overcome this problem has been discussed in this paper. The information update equations of the conventional information filter are modified in terms of fuzzy function of error and change of error, and the results have been found to be encouraging. The efficacy of the technique in preventing divergence is demonstrated in the context of tracking a maneuvering target also.     

基于模糊集合的证据理论信息融合方法

针对证据理论应用中基本概率分配函数(mass函数)和多传感器信息融合中各传感器测量数据的可靠程度均难以确定的问题,提出了一种基于模糊集合的证据理论信息融合方法.该方法首先利用模糊理论中的相关性函数来计算多传感器的相互支持程度;然后由隶属函数得到每个传感器提供信息的可信度;再将各传感器的支持度和可信度转化成基本概率分配函数即mass函数;最后利用证据理论对多传感器信息进行融合.仿真结果表明,该方法获得的结果具有更高的精度和可靠性.     

基于模糊神经网络的信息融合技术研究

针对信息融合技术中目标融合识别的问题,根据人工神经网络和模糊系统的各自特点,形成一种模糊神经网络模型。首先将模糊系统用神经网络的结构表示,并采用相应的学习算法训练模糊神经网络实现模糊推理功能。最后对模糊神经网络模型进行仿真实验和结果分析。     

一种基于模糊神经网络的融合故障诊断方法

通过构造模糊神经网络分类器,将基于模糊神经网络的数据融合方法引入到故障诊断中。通过在机组中设置温度传感器测量冷却介质进口温差、定子温度、冷却介质出口温差等模拟量,并求出各传感器对故障的隶属度,然后通过基于信息融合故障诊断算法确定故障原因,并且通过单传感器与多传感器融合结果的对比表明,多传感器数据融合诊断比单个传感器具有更高的准确性。     

模糊模式识别技术在水电仿真考核系统中的应用

提出了一种新的基于模糊模式识别的信息融合方法,即针对传统的确定性数学模型解决不确定问题存在的困难,建立了模糊模式识别的模型,并把此模型应用于吉林丰满水电数字仿真系统的成绩考核系统中,将参数的模糊集与正确等级模糊集进行匹配,降低了系统中1确定因素对考核系统的影响.经实验表明,此模型能正确解决不确定问题.     

Particle swarm optimization for determining fuzzy measures from data

Fuzzy measures and fuzzy integrals have been successfully used in many real applications. How to determine fuzzy measures is the most difficult problem in these applications. Though there have existed some methodologies for solving this problem, such as genetic algorithms, gradient descent algorithms and neural networks, it is hard to say which one is more appropriate and more feasible. Each method has its advantages and limitations. Therefore it is necessary to develop new methods or techniques to learn distinct fuzzy measures. In this paper, we make the first attempt to design a special particle swarm algorithm to determine a type of general fuzzy measures from data, and demonstrate that the algorithm is effective and efficient. Furthermore we extend this algorithm to identify and revise other types of fuzzy measures. To test our algorithms, we compare them with the basic particle swarm algorithms, gradient descent algorithms and genetic algorithms in literatures. In addition, for verifying whether our algorithms are robust in noisy-situations, a number of numerical experiments are conducted. Theoretical analysis and experimental results show that, for determining fuzzy measures, the particle swarm optimization is feasible and has a better performance than the existing genetic algorithms and gradient descent algorithms.     

数据融合的方法及应用研究

介绍了数据融合的定义、融合层次、融合方法及其应用领域，特别是在军事上，机器人控制及图像处理上的应用。在此基础上，分析了数据融合的研究热点及发展趋势。数据融合技术是一种用途广泛的数字信号及信息处理方法，它通过对大量的数据进行处理提纯，得到一组直观有效的数据，为进一步处理和判断控制提供精确的数据依据。     

一种概率聚类的新算法

提出了一种新的基于信息熵的概率聚类算法(Hierarchical Probabilistic Clustering Method,HPCM),HPCM算法和经典的模糊聚类算法FCM有着同样的聚类轨迹,因此,概率聚类和模糊聚类之间是紧密联系的.有关信息熵的大量研究成果可以帮助我们更深入地了解模糊聚类的本质.     

模糊推理和聚类分析的信息融合应用研究

针对采用多传感器信息融合在可视化远程监控系统中的应用,提出了一种基于聚类分析和模糊推理算法。运用多传感器信息融合模型建立二级数据处理单元系统,对环境信息进行多方面监测。聚类分析和模糊推理算法为准确地判断环境状态提供依据,进而达到校园公共环境的监控和预警。     

基于模糊矩阵博弈的网络安全威胁评估

在分布式网络安全积极防御系统中,需要进行多层次的信息融合。其中层次较高的威胁评估是根据网络安全态势感知结果,推测或判断威胁程度以及可能的行动方案。该文在分析博弈论中模糊矩阵博弈原理和网络空间威胁评估机理的基础上,提出了基于模糊矩阵博弈的网络安全威胁评估模型及其分析方法,并给出了计算实例与研究展望。     

基于模糊包含的粗糙集模型与模糊信任测度

提出基于模糊包含的模糊粗糙集模型,并给出其相关性质。讨论由模糊粗糙集的下、上近似所决定的模糊信任测度、模糊似然测度的性质并将之应用到随机模糊信息系统的属性约简中。     

基于概率犹豫信息集成方法的群决策模型*

现有犹豫模糊集在描述决策信息时会导致决策信息大量损失,因此文中基于概率犹豫模糊信息集成算子,构建多属性群决策模型.首先在概率犹豫模糊环境下引入Archimedean范数,定义概率犹豫模糊运算法则.基于该运算法则,提出广义概率犹豫模糊有序加权平均(GPHFOWA)算子和广义概率犹豫模糊有序加权几何(GPHFOWG)算子,并讨论它们的基本性质.然后分析GPHFOWA算子和GPHFOWG算子的常见形式和相互关系.最后运用提出的2类算子构建概率犹豫模糊多属性群决策模型,并且通过供应商的选择实例验证决策模型的可行性和有效性.     

基于模糊关系理论的信息系统划分方法

对信息系统划分方法的研究一直是信息系统开发研究的一个热点和难点。文章以模糊关系理论为基础,提出了一种信息系统模糊划分方法（ISFD）,并给出了一个应用实例。使用ISFD方法划分的子系统具有良好的系统柔性。ISFP方法简单、实用,具有较高的应用价值。     

模糊划分的决策树方法

有许多优化问题中，目标值是连续的。对这类问题，首先对目标值进行离散化，再采用决策树方法提取规则。在一定程度上，相比直接对连续的目标值优化可提高正确率，并增加结果的可理解性。为了克服分段划分带来的突变性，可将目标值进行模糊划分，再采用决策树方法提取规则，这样进一步可提高正确率。     

Fuzzy decision fusion of complementary experts based on evolutionary cepstral coefficients for phoneme recognition

Optimal representation of acoustic features is an ongoing challenge in automatic speech recognition research. As an initial step toward this purpose, optimization of filterbanks for the cepstral coefficient using evolutionary optimization methods is proposed in some approaches. However, the large number of optimization parameters required by a filterbank makes it difficult to guarantee that an individual optimized filterbank can provide the best representation for phoneme classification. Moreover, in many cases, a number of potential solutions are obtained. Each solution presents discrimination between specific groups of phonemes. In other words, each filterbank has its own particular advantage. Therefore, the aggregation of the discriminative information provided by filterbanks is demanding challenging task. In this study, the optimization of a number of complementary filterbanks is considered to provide a different representation of speech signals for phoneme classification using the hidden Markov model (HMM). Fuzzy information fusion is used to aggregate the decisions provided by HMMs. Fuzzy theory can effectively handle the uncertainties of classifiers trained with different representations of speech data. In this study, the output of the HMM classifiers of each expert is fused using a fuzzy decision fusion scheme. The decision fusion employed a global and local confidence measurement to formulate the reliability of each classifier based on both the global and local context when making overall decisions. Experiments were conducted based on clean and noisy phonetic samples. The proposed method outperformed conventional Mel frequency cepstral coefficients under both conditions in terms of overall phoneme classification accuracy. The fuzzy fusion scheme was shown to be capable of the aggregation of complementary information provided by each filterbank.     

基于模糊神经网络和证据理论的列车智能控制系统多信息融合故障诊断研究

针对列车智能控制系统故障诊断中的多故障特征信息输入时的时变、冗余、不确定性和空间分布性,给出了一种列车智能控制系统多信息融合故障诊断的系统结构。讨论了采用模糊神经网络进行特征层融合和证据理论进行决策层融合相结合的列车智能控制系统多信息融合故障诊断方法。故障诊断实例的结果表明:该方法能够有效地提高诊断的可信度,减小诊断的不确定性。     

模糊神经网络信息融合方法在机器人避障中的应用

基于Takagi—Sugeno(T—S)模型的模糊神经网络不但具有模糊逻辑和神经网络两者的优点，又具有很好的学习能力。将基于T—S模型的模糊神经网络的信息融合算法应用在移动机器人的避障运动中，采用了多个超声测距传感器探测障碍物的距离和方向，经过模糊神经网络信息融合后，实现了机器人对障碍物和环境类型的识别以及无冲突的运动。实验表明：此方法能够使机器人安全避障。