On-line Weighted Least Squares Kernel Method for Nonlinear Dynamic Modeling

Support vector machines （SVM） have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on rolling optimization method and on-line learning strategies, a novel approach based on weighted least squares support vector machines （WLS-SVM） is proposed for nonlinear dynamic modeling. The good robust property of the novel approach enhances the generalization ability of kernel method-based modeling and some experimental results are presented to illustrate the feasibility of the proposed method.     

基于双边迭代奇异值分解的递推子空间辨识方法

引入双边迭代奇异值分解算法，通过一系列的QR分解，用两个矩阵分别逼近奇异值分解的主要左、右奇异向量，用一个三角矩阵逐渐逼近主要的特征值，从而取代了原始MOESP子空间辨识算法中的奇异值分解步骤。通过用一系列Givens变换来实现QR分解的数据更新，实现了此类子空间方法的在线递推辨识。仿真表明，该方法可以有效地对系统的极点进行跟踪。     

产聚β-羟基丁酸芽孢杆菌的拉曼光谱快速筛选研究

【目的】探索快速筛选产生物塑料聚β-羟基丁酸(PHB)芽孢杆菌的光学手段,以期筛选到以葡萄糖或蔗糖为发酵底物的高产PHB优良菌株。【方法】应用拉曼光镊技术快速收集分离株的单个细胞拉曼光谱,分析其光谱特征。【结果】基于拉曼光谱特征峰可快速辨别细胞类型和细胞PHB含量,菌落细胞的PHB拉曼信号强度与菌株的PHB发酵能力有相关性;从不同土壤样品中分离的菌株,45%以上具有产PHB的能力,其中S-C-2菌株在3%蔗糖下PHB最大产量为5.27g/L,PHB占细胞干重70%;拉曼光谱监测发酵过程显示,不同发酵阶段发酵液的PHB总含量与单个芽孢杆菌细胞1732cm~(-1)峰的平均信号强度线性相关。【结论】拉曼光谱可以简单快速分选产PHB的芽孢杆菌,实时监测PHB发酵进程。     

Novel Face Recognition Method by Combining Spatial Domain and Selected Complex Wavelet Features

A novel face recognition method based on fusion of spatial and frequency features was presented to improve recognition accuracy.Dual-Tree Complex Wavelet Transform derives desirable facial features to cope with the variation due to the illumination and facial expression changes.By adopting spectral regression and complexfusiontechnologiesrespectively,twoimproved neighborhood preserving discriminant analysis feature extraction methods were proposed to capture the face manifold structures and locality discriminatory information.Extensive experiments have been made to compare the recognition performance of the proposed method with some popular dimensionality reduction methods on ORL and Yale face databases.The results verify the effectiveness of the proposed method.     

Support Vector Machine Ensemble Based on Genetic Algorithm

Support vector machines （SVMs） have been introduced as effective methods for solving classification problems. However, due to some limitations in practical applications, their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble （DGE）, is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE. Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs, hagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.     

一类时滞不确定性系统的鲁棒H2/H∞状态估计

研究了一类时滞不确定性系统的状态估计问题 ,其中系统的参数不确定性是时变和模有界的。首先证明了时滞不确定性系统二次稳定且H∞ 范数小于指定的上界的充分条件 ,然后利用修正的Riccati型不等式推导了使得估计系统同时满足二次稳定和鲁棒H2 /H∞ 性能的线性估计器存在的充分条件 ,通过求解两个代数Riccati方程的正定解 ,得到了该估计器的参数表示。仿真试验表明了该设计方法的有效性和可行性。     

基于变分贝叶斯理论的不确定厚尾噪声滤波方法

为了解决非线性滤波中量测噪声呈厚尾分布且统计特性不确定的问题,提出一种基于Pearson Type VII分布的自适应滤波算法.针对传统鲁棒卡尔曼滤波器因尺度矩阵和自由度参数固定不变而无法自适应调整的问题,以容积卡尔曼滤波器为基础,选择Pearson Type VII分布对厚尾噪声进行建模,将传统鲁棒滤波固定自由度参数的估计转化为Pearson Type VII分布中可自适应调整的双自由度参数的估计,并通过inverse Wishart和Gamma分布描述尺度矩阵、双自由度参数和辅助参数的先验分布,利用遗忘因子对各参数进行时间更新;基于变分贝叶斯理论,对系统状态、尺度矩阵、双自由度参数和辅助参数形成的联合后验概率密度函数进行变分迭代,实现对系统状态和未知厚尾噪声的联合估计.仿真结果表明,在不确定厚尾噪声条件下,本文算法的滤波精度高于传统鲁棒容积卡尔曼滤波.     

一种参数不确定性系统的鲁棒状态估计方法

研究具有时变和模有界的参数不确定性系统的状态估计问题。针对目前鲁棒状态估计中不确定性参数仅出现于状态矩阵和输出矩阵的问题，介绍了一种系统状态方程和输出方程的所有矩阵中都含有不确定性参数的状态估计算法。该算法通过求解两个代数Riccati方程的正定解，得到了使得估计过程二次稳定的状态估计器。此算法不但经过了理论证明，而且具体的仿真试验也表明了设计方法的有效性和可行性。     

AOI交会触发服务器消息过滤方法

针对分布式虚拟环境(DVE)系统中服务器为系统性能瓶颈问题,提出了一种新的基于DVE系统化身兴趣区域(AOI)交会触发的服务器消息过滤方法.较之于传统的基于AOI方法的服务器消息优化方法,它通过在客户之间直接进行消息交互而减少客户与服务器之间的通信数量,并最终减少服务器的通信负载.针对该方法进行了理论分析,一系列的仿真实验也验证了理论的正确性.