基于核密度最大熵方法的杂系混合信号盲分离*

传统的独立分量分析方法普遍存在的非线性评价函数只能凭经验选取,当混合信号同时包含超高斯和亚高斯信号时,算法难以取得很好的分离效果。利用基于随机变量矩的核密度最大熵方法对非线性函数进行直接估计,提出了基于核密度最大熵方法的杂系混合信号盲分离算法,成功地分离了杂系混合信号。仿真结果验证了算法的有效性。     

Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm

In the paper, a three-level thresholding method for image segmentation is presented, based on probability partition, fuzzy partition and entropy theory. A new fuzzy entropy has been defined through probability analysis. The image is divided into three parts, namely, dark, gray and white part, whose member functions of the fuzzy region are Z-function and Π-function and S-function, respectively, while the width and attribute of the fuzzy region can be determined by maximizing fuzzy entropy. The procedure for finding the optimal combination of all the fuzzy parameters is implemented by a genetic algorithm with appropriate coding method so as to avoid useless chromosomes. The experiment results show that the proposed method gives good performance.     

Design of a complex system based on the maximum entropy principle

A complex system is a system composed of many dynamic elements with mutual interactions. This paper proposes a unified approach for the design of an information processing system using a complex system. The method of design is based on the maximum entropy principle. After a detailed explanation, the proposed method is applied to the design of a spatial filter using a complex system. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996.     

Application of maximum entropy principle for reliability-based design optimization

The maximum entropy principle (MEP) is used to generate a natural probability distribution among the many possible that have the same moment conditions. The MEP can accommodate higher order moment information and therefore facilitate a higher quality PDF model. The performance of the MEP for PDF estimation is studied by using more than four moments. For the case with four moments, the results are compared with those by the Pearson system. It is observed that as accommodating higher order moment, the estimated PDF converges to the original one. A sensitivity analysis formulation of the failure probability based on the MEP is derived for reliability-based design optimization (RBDO) and the accuracy is compared with that by finite difference method (FDM). Two RBDO examples including a realistic three-dimensional wing design are solved by using the derived sensitivity formula and the MEP-based moment method. The results are compared with other methods such as TR-SQP, FAMM + Pearson system, FFMM + Pearson system in terms of accuracy and efficiency. It is also shown that an improvement in the accuracy by including more moment terms can increase numerical efficiency of optimization for the three-dimensional wing design. The moment method equipped with the MEP is found flexible and well adoptable for reliability analysis and design.     

New measures of weighted fuzzy entropy and their applications for the study of maximum weighted fuzzy entropy principle

Keeping in view the non-probabilistic nature of experiments, two new measures of weighted fuzzy entropy have been introduced and to check their authenticity, the essential properties of these measures have been studied. Under the fact that measures of entropy can be used for the study of optimization principles when certain partial information is available, we have applied the existing as well as the newly introduced weighted measures of fuzzy entropy to study the maximum entropy principle.     

基于最大熵估计的支持向量机概率建模

提出一种基于最大熵估计的支持向量机概率建模方法．针对传统的支持向量机方法不能提供后验概率的输出问题,从信息熵的角度采用最大熵估计方法,直接对支持向量机输出进行后验概率建模．实验结果表明,与同类算法相比,所提出的基于最大熵估计的概率建模方法具有优良的性能．     

约束条件下BN参数最大熵模型扩展学习算法

在很多智能系统的参数建模时,用户往往面对建模样本稀少的困境。针对在小数据集条件下贝叶斯网络（BN）参数建模的问题,提出了一种约束数据最大熵BN参数学习算法（CDME）。首先利用小数据集估算BN参数,随后把定性的专家经验转换为不等式约束,并利用Bootstrap算法生成满足约束的一组参数候选集,再根据信息最大熵进行加权计算出BN参数。实验结果表明,当数据量充分时,CDME参数学习算法与经典的MLE算法的学习精度近似,表明了算法的正确性;在小数据集条件下,利用CDME算法,可以对BN进行参数建模,学习精度优于MLE算法和QMAP算法。CDME算法在实际故障诊断样本数据相对稀缺的条件下,获取了诊断BN模型参数,在此基础上完成的诊断推理结果也印证了算法的有效性,为小数据集条件下的参数建模提供了一条新途径。     

A modular neural network for direction-of-arrival estimation of two sources

This work addresses the problem of estimating the direction-of-arrival (DOA) of two sources using an array of sensors. This problem is mostly useful in radar applications, where we have few targets at each range bin. Super-resolution algorithms, such as maximum likelihood (ML) estimation and multiple signal classification (MUSIC), have been applied to this problem, but the former involves high computation efforts, while the later has poor estimation performance for coherent sources. In this work, we propose a DOA estimation network, named RBF-AML, which combines the approximated ML (AML) estimator and a radial basis function (RBF) neural network (NN). In the proposed RBF-AML network, the entire two dimensional DOA space is divided into multiple sectors covered by RBF experts. The AML function is then used as a mediator among the experts and selects the most suitable one as the final output of the system. The performance of the RBF-AML network for a two coherent sources case in a Y shape array configuration is evaluated. We show that the performance of the RBF-AML network is similar to the performance of the classical AML DOA estimation for various signal-to-noise ratios (SNRs), phase of the correlation coefficient and signal-to-interference ratios (SIRs). Furthermore, the RBF-AML network requires fewer computational efforts than the classical AML DOA estimation and therefore is an attractive choice for real-time applications.     

基于最大累积剩余熵的红外图像分割

经典阈值化分割方法是常用的图像分割方法之一,但对于低对比度,低信噪比的红外图像分割效果不理想,将累积剩余熵运用于红外图像分割,即寻求使得累积剩余熵最大的灰度值作为分割阈值,实验结果表明:基于最大累积剩余熵的图像分割方法是一种有效的红外图像分割方法,比起经典的阈值分割方法,分割红外图像的效果要好。     

基于贝叶斯的改进WSNs信任评估模型

基于贝叶斯和熵,提出一种改进的WSNs信任评估模型。考虑到非入侵因素带来的网络异常行为,引入异常衰减因子,利用修正后的贝叶斯方程估算直接信任,并利用滑窗和自适应遗忘因子进行更新。根据直接信任的置信水平确定其是否足够可信来作为综合信任,减少网络能耗,并降低恶意反馈的影响。如果直接信任不足够可信,计算间接信任来获得综合信任,利用熵来对不同的推荐赋予权重,克服主观分配权重带来的局限性,加强模型的适应性。仿真实验表明,该模型能够有效检测恶意节点,具有较高的检测率和较低的误检率,同时在很大程度上降低了网络的能量消耗。     

基于最大熵模型的中国人名自动识别

用最大熵模型自动识别中国人名。首先对语料库的词性进行角色替换,然后用特征模板从角色替换后的语料库中提取出特征集,接着用IIS算法训练特征集的最大熵参数,最后用viterbi算法对初分词文本进行角色标注,并在角色序列的基础上进行模式最大匹配,从而实现中国人名的自动识别。在封闭测试实验中,识别准确率、召回率、F-值分别达到了85.4%、91.2%、88.2%。     

On the performance of the flexible maximum entropy distributions within partially adaptive estimation

The partially adaptive estimation based on the assumed error distribution has emerged as a popular approach for estimating a regression model with non-normal errors. In this approach, if the assumed distribution is flexible enough to accommodate the shape of the true underlying error distribution, the efficiency of the partially adaptive estimator is expected to be close to the efficiency of the maximum likelihood estimator based on knowledge of the true error distribution. In this context, the maximum entropy distributions have attracted interest since such distributions have a very flexible functional form and nest most of the statistical distributions. Therefore, several flexible MaxEnt distributions under certain moment constraints are determined to use within the partially adaptive estimation procedure and their performances are evaluated relative to well-known estimators. The simulation results indicate that the determined partially adaptive estimators perform well for non-normal error distributions. In particular, some can be useful in dealing with small sample sizes. In addition, various linear regression applications with non-normal errors are provided.     

构建贝叶斯网络本质图的新方法

等价类学习是贝叶斯网络结构学习的一个重要分支,而本质图是贝叶斯网络等价类的图形表示,是进行等价类学习的有力工具。针对求解贝叶斯网络结构本质图存在的繁琐问题,提出了一种构建贝叶斯网络本质图的组合算法。该算法从初始非循环有向图开始,对所有有向边进行排序,保持V-结构中的边不变,将不参与V-结构的有向边转化为无向边,依次根据三条规则判定各条无向边在本质图中的方向。给出了算法的理论证明,通过具体案例分析验证了算法的有效性。     

基于最大熵方法进行动词搭配的自动标注

搭配是汉语自动句法分析的重要知识源,而动词是句法分析的核心和前提。通过对已标注真实文本的分析,构造了动词搭配对的上下文变量信息特征模板,给出利用最大熵方法抽取动词—动词搭配,对待测的1 000句汉语句子应用最大熵方法自动识别出搭配,其中封闭测试抽取正确率为85.6%,召回率达到70.6%。     

基于贝叶斯网络的故障诊断策略优化方法

通过分析设备故障诊断与维修所面临的主要问题以及当前常用诊断策略存在的局限性，研究基于贝叶斯网络的故障诊断策略优化方法。提出了适合于表达诊断问题的基于故障假设一观测一维修操作节点的贝叶斯网络结构，阐述了基于贝叶斯网络的故障诊断策略优化方法的基本思想和优化算法。该方法综合考虑了多故障、有观测操作以及操作之间有依赖关系等情况。最后通过应用实例，证实了该方法在信患不确定条件下进行诊断与维修决策的有效性。     

高维数据固有维数的自适应极大似然估计

如何确定高维数据的固有维数是降维成功与否的关键。基于极大似然估计（MLE）的维数估计方法是一种新近出现的方法,实现简单,选择合适的近邻能取得不错的结果。但当近邻数过小或过大时,均有比较明显的偏差。其根本原因是没有考虑每个点对固有维数的不同贡献。在充分考虑数据集的分布信息之后,提出了一种改进的MLE——自适应极大似然估计(AMLE)。实验表明,无论在合成数据集还是真实数据集上,AMLE较MLE在估计准确度上均有很大的提高,对近邻数的变化也不甚敏感。     

基于改进最大熵谱估计的长输管道内检测器

对长输管道内检测器的机械结构、超声检测、检测定位、检测数据及信号处理等进行了分析,短时间序列、高分辨率、强抗噪能力的功率谱估计是管道超声内检测的关键技术,针对Burg最大熵谱估计存在的问题,从减小递推算法初始阶段误差出发,提出二阶预测误差滤波器系数倒推法,由二阶滤波器系数修正一阶反射系数,保证递推初始阶段最大熵原则,以适应短时间序列谱估计,研制了长输管道内检测器样机,经试验管道检测证明,腐蚀缺陷检测精度较高,内外定位较准确,应用前景很好.     

基于Bayes与多层Bayes估计的WSN链路选择算法

无线传感器网络（WSN）节点能量有限,采用传统的链路选择的方法（经验法）进行链路选择,需要发送大量的数据包作为测试样本,这在WSN中是不合适的。设计了两种基于Bayes估计与一种基于多层Bayes估计的WSN链路选择算法,分别记为BLSP-B1、BLSP-B2、BLSP-HE。仿真实验发现,在小样本的条件下,BLSP-B1、BLSP-B2、BLSP-HE选择高质量的链路的概率比经验法要高出10%～20%,其中BLSP-HE算法最稳健,性能较好。     

基于最大熵模型和规则的中文姓名识别

中文姓名识别是中文信息处理的一项重要技术,识别的召回率对其它需要以姓名识别为基础的中文信息处理技术有至关重要的影响。提出了一种统计模型和处理规则相结合的中文姓名识别方法：首先以最大熵模型识别潜在姓氏,而后再通过判定规则作进一步处理。真实语料的开放测试表明,该方法在召回率方面有明显的优势,可以达到94%以上的召回率,同时能保证较高的准确率。     

基于边界梯度控制的最大熵阈值分割方法

结合梯度和灰度这两种图像的本质特征,提出一种基于边界梯度控制的最大熵阈值分割方法。该方法首先定义了一种边界梯度控制函数来定量分析图像中细节信息的丰富程度,通过该函数的局部极大值确定可能的分割阈值的集合,然后根据最大熵原理在该集合中选取最优阈值,最终实现图像的二值化分割。实验结果表明该方法的分割结果由于保留了丰富的细节信息,能够更好地体现图像语义,且该方法亦具有一定的抗噪性。