一种求解非线性整数规划的分布估计算法

分布估计算法是遗传算法和统计学习的结合,通过统计学习的手段建立解空间内个体分布的概率模型,对概率模型随机采样产生新的群体,如此反复进行,实现群体的进化。将分布估计算法推广应用到整数规划的解空间中,提出一种求解整数规划的新算法,经数值实验表明该算法有效。     

基于概率主成分分析的人脸识别

人脸自动识别是模式识别和图像处理等学科的一大研究热点,在身份鉴别、信用卡识别、护照核对以及监控系统等方面有着广泛的应用。提出一种基于概率主成分分析方法(PPCA)的人脸识别,该方法与传统的主成分分析(PCA)相比,克服了简单的“丢弃”其他非主成分因子,在PPCA中将“丢弃”因子作为噪声成分进行估计．同时PPCA方法是一种基于概率模型的方法,因此很容易延伸为混合模型,对于PPCA概率模型参数,提出利用EM算法对其进行估计。用两个不同的数据集(姿势表情变化集和光照变化集),将PPCA人脸识别算法和传统的PCA算法进行比较,基于PPCA的人脸识别算法中的“丢弃”方差的收敛速度快于传统的PCA算法。实验结果表明．无论是姿势表情变化集,还是光照变化集,PPCA算法的识别率都优于传统的PCA识别算法。     

精英类克隆选择算法平均收敛速度估计

克隆选择算法收敛速度估计是算法研究的一个难问题,目前还是处于初始的研究阶段。本文对一大类精英保持策略克隆选择算法的收敛速度问题进行了研究。首先利用算法种群中最佳个体的定向转移概率导出最佳个体的转移概率矩阵,针对实际应用中由于算法种群规模过大而导致该矩阵求取较困难的问题,将最佳个体的转移概率矩阵构造成满足一定条件的矩阵范数,从而提出一种更为简单有效的算法平均收敛速度估计的新方法。对不同的精英保持策略克隆选择算法进行了收敛速度估计仿真实验,其结果表明了该估计方法的有效性。     

微分进化算法的优化研究及其在聚类分析中的应用

为了使微分进化算法在进化过程中充分挖掘和利用历史数据信息,提高它的全局搜索能力和收敛速度,提出了一种基于主成分的微分进化算法PCADE。该算法将种群空间映射到主成分空间从而得到一个由主成分构成的种群空间,在进化过程中前m个主成分构成的个体可以直接进入下一代的进化,而剩余的N-m个个体则从原种群和主成分种群空间中选择出适应度值较高的个体进入下一代。实验结果表明改进算法在聚类分析中取得了较好的结果。     

小生境共享克隆选择算法及其在TSP中的应用

克隆选择算法（简写为CSA）是基于生物免疫学中的克隆选择原理而提出的一种寻优技术,此算法具有收敛速度快,局部搜索能力强的优点;但也有易陷入局部收敛的不足。小生境是生物学中物体生存的一种组织结构,基于这种组织结构产生了小生境共享思想,即对包含相似个体较多的物种,抑制此物种中个体的生存机会,从而给稀有物种以生存机会;现借鉴小生境共享思想提出了小生境共享克隆选择算法（简写为NSCSA）,理论分析和仿真实验均表明NSC-SA算法通过提高迭代种群个体多样性,全局搜索性能得到了较大提高。     

人机交互的Copula和协同优化算法在分布估算中的应用

人机交互的Copula函数和协同优化理论相结合的算法能有效解决分布估计算法在优化变量强相关问题的过程中存在的效果差、模型估计时间长等问题.该方法首先将Copula函数作为各个边缘分布函数的连接函数来建立一个解分布的概率模型;然后产生种群的过程中人工的选择其他种群中的优良个体,替换本种群中较差的个体并采用协同优化的思想,在种群进化时充分考虑其他种群中的个体的影响,最大限度的保证向着最优进化的方向发展.将该方法应用在对3个相互独立的种群进化上,其实验参数反演结果表明,该算法不但增强了优化的效果,而且还有效的避免了早熟,加快了算法的收敛速度.同时也表明了人机交互的过程在解决较为复杂的实际问题中的有效性和可操作性.     

基于双特征值极限分布的合作频谱感知算法

以最大最小特征值之差作为检测统计量,从提高算法判决门限估计精度出发,利用双特征值极限分布,用双特征值估计判决门限,提出了基于双特征值极限分布的频谱感知算法。相比于单特征值门限方法,理论上证明了用双特征值极限分布估计的门限小于或等于用单特征值极限分布估计的门限,提高了算法检测性能。仿真结果表明,该算法不受噪声不确定性影响,不需要主用户信息,并且在低虚警概率、采样次数相对少的情况下,检测性能优于最大最小特征值之差频谱感知算法。     

三维LMS运动估计的迭代算法和线性算法

本文阐述了引入特征点集误差分布的最小均方（ＬＭＳ）运动估计问题，最优运动 估计本质是上６自由度的非线性问题，本文提出推广的主重合定理和运动参数分解定量，将其简化成３自由度的纯旋转问题，我们给出最优运动参数估计的线性算法，兼有运动 估计的最优性质和能够实时实现的优点；同时给出一个迭代算法，其性能也是最优的，并且收敛性能更好。     

帧间约束MMSE语音增强算法

本文主要讨论基于语音短时谱估计的语音增强算法，在语音短时谱幅度最小均主误差估计（ＭＭＳＥ）的基础上，本文提出了一种基于帧间频域分布约束的改进的语音短时谱幅度ＭＭＳＥ估计算法，该算法充分利用了语音分析帧之间存在的相关性，并以频域分布约束的形式结合到估计式中，考虑到在ＭＭＳＥ估计式中需要利用语音频谱的分布，本文采取了两种解决办法，利用高斯分布模型进行近似；利用语音材料进行统计得到近似的分布模型，新算法     

基于EM参数“软”估计的SAR图像分割

借助EM算法和模糊理论,提出了一种基于参数"软"估计和Markov随机场的SAR图像无监督分割方法。首先利用多维空间的EM算法估计随机场的模型参数,并根据随机场模型参数分别计算观测数据的条件概率和标记图像的先验概率,继而根据最大后验概率准则将图像分成具有相似统计特性的同质区域,重复以上步骤直至收敛。通过与传统的参数"硬"估计分割算法的实验比较,该算法能更好保持图像边缘细节,区域连通性更好。     

一种基于分布估计的离散粒子群优化算法

 本文提出了一种基于分布估计的离散粒子群优化算法.提出的新算法突破了传统粒子群速度-位移搜索模型的局限,且种群中的每个粒子具有更全面的学习能力,从而能够有效地解决组合优化问题.仿真实验结果表明提出的新算法的性能优于现有的其它几种离散粒子群优化算法.     

Clustering and learning Gaussian distribution for continuous optimization

Since the Estimation of Distribution Algorithm (EDA) was introduced, different approaches in continuous domains have been developed. Initially, the single Gaussian distribution was broadly used when building the probabilistic models, which would normally mislead the search when dealing with multimodal functions. Some researchers later constructed EDAs that take advantage of mixture probability distributions by using clustering techniques. But their algorithms all need prior knowledge before applying clustering, which is unreasonable in real life. In this paper, two new EDAs for continuous optimization are proposed, both of which incorporate clustering techniques into estimation process to break the single Gaussian distribution assumption. The new algorithms, Clustering and Estimation of Gaussian Network Algorithm based on BGe metric and Clustering and Estimation of Gaussian Distribution Algorithm, not only show great advantage in optimizing multimodal functions with a few local optima, but also overcome the restriction of demanding prior knowledge before clustering by using a very reliable clustering technique, Rival Penalized Competitive Learning. This is the first time that EDAs have the ability to detect the number of global optima automatically. A set of experiments have been implemented to evaluate the performance of new algorithms. Besides the improvement over some multimodal functions, according to the No Free Lunch theory, their weak side is also showed.     

Infrared Small Target Detection Using PPCA

Probabilistic PCA (PPCA) is an extension of PCA which reformulated PCA in a probabilistic framework. In this paper we propose a infrared small target detection algorithm using PPCA analogous to the face detection scheme using PCA, or known as “eigenface”. By computing the parameters of PPCA, we map the input vector from the image onto a subspace. After reconstructing the vector, the distance between the original vector and the reconstructed one will indicate the possibility of the input being a target. Experimental results show the effectiveness of this algorithm compared with other methods.     

建立在一般结构Gauss网络上的分布估计算法

提出了一种建立在一般结构Gauss网络上的分布估计算法。一方面,它无需进行Gauss网络结构的学习,从而大大减少了计算量,另一方面,一般结构Gauss网络不是近似网络,因而可获得精度很高的联合概率密度函数。针对该网络,采用了一种无需计算条件概率密度函数的产生样本方法,有效地减少了网络参数学习的计算开销。实验结果表明,与已有建立在非一般结构Gauss网络上的高阶分布估计算法相比,本文算法具有更高的稳定性和更强的寻优能力。     

Radar HRRP Statistical Recognition: Parametric Model and Model Selection

Statistical modeling for radar high-resolution range profile (HRRP) is a challenging task in radar HRRP statistical recognition. Theoretical analysis and experimental results show that elements in an HRRP sample are statistically correlated and non-Gaussian distributed. First, this paper introduces three joint-Gaussian models, i.e., subspace approximation model, probability principal components analysis (PPCA) model and factor analysis (FA) model, into radar HRRP statistical recognition. Due to the experimental results, we can have the conclusion that the jointly non-Gaussian distributed HRRP samples approximately follow the joint-Gaussian distribution described by FA model. Therefore, we can apply FA model to radar HRRP statistical recognition rather than a joint-Gaussian mixture model, e.g., PPCA mixture model or FA mixture model, which is a more accurate choice for modeling non-Gaussian distributed correlations in multidimensional data but with high learning complexity and large computation burden, and the difficulty in the statistical modeling for HRRP samples is largely reduced. Second, this paper concerns model selection of FA model in radar HRRP statistical recognition, in which there are two issues, i.e., the partition of target-aspect frames and the determination of the number of factors in each frame. Based on the Akaike information criterion (AIC) and the Bayes' information criterion (BIC), an iterated algorithm for model selection is proposed in this paper, which can automatically give the optimal aspect-frame boundaries and determine the optimal number of factors in each aspect-frame. The recognition experiments based on measured data show that the proposed adaptive partition approach can further improve the recognition performance with higher recognition efficiency.     

Reducing the dimensionality of plant spectral databases

Ground-based measurements of plant reflectance and transmittance are essential for remote sensing projects oriented toward agriculture, forestry, and ecology. This paper examines the application of principal components analysis (PCA) in the storage and reconstruction of such plant spectral data. A novel piecewise PCA approach (PPCA), which takes into account the biological factors that affect the interaction of solar radiation with plants, is also proposed. These techniques are compared through experiments involving the reconstruction of reflectance and transmittance curves for herbaceous and woody specimens. The spectral data used in these experiments were obtained from the Leaf Optical Properties Experiment (LOPEX) database. The reconstructions were performed aiming at a root-mean-square error lower than 1%. The results of these experiments indicate that PCA can effectively reduce the dimensionality of plant spectral databases from the visible to the infrared regions of the light spectrum, and that the PPCA approach can further maximize the accuracy/cost ratio of the storage and reconstruction of plant spectral reflectance and transmittance data.     

基于改进蚁群算法的WDM光网络恢复优化

针对WDM光网络的恢复,提出一种基于蚁群算法(ACA)的WDM光网络恢复优化算法,算法以WDM光网络的虚拓扑为主要研究对象,模拟蚂蚁寻址方式获得网络业务受损节点恢复方案.在优化过程中,结合免疫算法的变异操作对全局业务分配进行自适应调节,以获得更高的故障恢复率水平,最终实现网络受损业务恢复,使整个网络资源利用率达到最优.仿真结果表明,该算法可满足较高的恢复率需求情况,并可应用于任意形式WDM网络拓扑结构.     

Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models

In this paper, we present an original method for the three-dimensional (3-D) reconstruction of the scoliotic rib cage from a planar and a conventional pair of calibrated radiographic images (postero-anterior with normal incidence and lateral). To this end, we first present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers (PPCA). This method is based on the stochastic expectation maximization (SEM) algorithm. Parameters of this mixture model are used to constrain the 3-D biplanar reconstruction problem of scoliotic rib cage. More precisely, the proposed PPCA mixture model is exploited for dimensionality reduction and to obtain a set of probabilistic prior models associated with each detected class of pathological deformations observed on a representative training scoliotic rib cage population. By using an appropriate likelihood, for each considered class-conditional prior model, the proposed 3-D reconstruction is stated as an energy function minimization problem, which is solved with an exploration/selection algorithm. The optimal 3-D reconstruction then corresponds to the class of deformation and parameters leading to the minimal energy. This 3-D method of reconstruction has been successfully tested and validated on a database of 20 pairs of biplanar radiographic images of scoliotic patients, yielding very promising results. As an alternative to computed tomography-scan 3-D reconstruction this scheme has the advantage of low radiation for the patient, and may also be used for diagnosis and evaluation of deformity of a scoliotic rib cage. The proposed method remains sufficiently general to be applied to other reconstruction problems for which a database of objects to be reconstructed is available (with two or more radiographic views).