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.     

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.     

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。     

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。     

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。     

Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis

To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines （MPSVMs） using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.     

基于神经网络的不稳定时滞对象控制

针对过程控制工业中的一类不稳定时滞对象存在的难以稳定、鲁棒性差、对输入的变化和扰动十分敏感的问题,采用了基于神经网络的双自由度控制结构.在利用内环控制器镇定对象并且提高系统反应速度、减轻输出的振荡的同时,结合Guillermo等提出的比例-积分-微分(PID)参数镇定区域理论优化设计外环BP神经网络控制器的学习范围、学习方式和初始参数,改善系统设定值跟踪和扰动抑制的性能,提高系统鲁棒性.仿真结果表明,即使在建模有误差的情况下,该控制结构仍能比传统双自由度PID控制有更好的控制效果和鲁棒性.     

一种网络演化MG模型研究

将少数者博弈模型作为描述经纪人竞争的动力学模型,并考虑了现实中存在的经纪人学习模仿机制,研究了经纪人在小世界网络下的通过学习和模仿自组织形成群落的现象,同时进行了仿真实验.分析了回报比率β和重连概率p与经纪人群落演化的关系,得出了在不同参数下群落规模分布的规律,以及模型系统方差变化的规律,为更好地研究社会经济系统中的涌现现象提供了一种新的方法.