关于二维主成分分析方法的研究

The principal component analysis (PCA), or the eigenfaces method, is a de facto standard in human face recognition. Numerous algorithms tried to generalize PCA in different aspects. More recently, a technique called two-dimensional PCA (2DPCA) was proposed to cut the computational cost of the standard PCA. Unlike PCA that treats images as vectors, 2DPCA views an image as a matrix. With a properly defined criterion, 2DPCA results in an eigenvalue problem which has a much lower dimensionality than that of PCA. In this paper, we show that 2DPCA is equivalent to a special case of an existing feature extraction method, i.e., the block-based PCA. Using the FERET database, extensive experimental results demonstrate that block-based PCA outperforms PCA on datasets that consist of relatively simple images for recognition, while PCA is more robust than 2DPCA in harder situations.     

基于遗传BP网络的模拟电路故障诊断方法及其应用

针对BP网络诊断模拟电路故障时存在网络结构复杂且可能出现误诊断的不足,提出一种小波变换、遗传算法与神经网络相结合的模拟电路故障诊断的新方法.该方法使用节点电压信号经小波变换、主元分析和归一化处理来实现故障特征的提取,以减少信号的冗余;由于BP网络易陷入局部最优,采用遗传箅法来优化BP网络的结构和初始权值分布,使优化后的神经网络具有较好的收敛性能.最后结合电路实例,对文中提出诊断方法的原理与实现进行了较深入的分析,建立了该方法的测试系统,并通过工程应用效果进一步验证了文中方法的正确性.     

基于二阶双向二维主成分分析的人脸识别方法

为了克服光照变化较大的情况对识别率的影响,提出基于二阶双向二维主成分分析(Sec-(2D)2PCA)的人脸识别方法。丢弃提取人脸图像的(2D)2PCA的前几个反映光照信息的主成分。在剩余图像中再次使用(2D)2PCA方法。Yale人脸库B和Yale人脸库上的试验结果表明,该方法在识别性能上优于2DPCA、(2D)2PCA、Sec-2DPCA方法。     

基于数据库编程语言的海量邮件数据分类技术

针对海量邮件数据的处理需求和实际业务需要,设计了基于数据库编程语言的海量邮件自动分类系统。该系统由特征学习模块、数据库查询模块和贝叶斯分类模块3部分构成。结合贝叶斯分类算法,利用PL/SQL语言与数据库交互时的高效性特点,在ORACLE PL/SQL存储过程中完成对未知邮件的特征提取和表示,实现对海量邮件数据的有效分类。     

A Method of Face Recognition Based on Fuzzy c-Means Clustering and Associated Sub-NNs

The face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. In this paper, we present a method for face recognition based on parallel neural networks. Neural networks (NNs) have been widely used in various fields. However, the computing efficiency decreases rapidly if the scale of the NN increases. In this paper, a new method of face recognition based on fuzzy clustering and parallel NNs is proposed. The face patterns are divided into several small-scale neural networks based on fuzzy clustering and they are combined to obtain the recognition result. In particular, the proposed method achieved a 98.75% recognition accuracy for 240 patterns of 20 registrants and a 99.58% rejection rate for 240 patterns of 20 nonregistrants. Experimental results show that the performance of our new face-recognition method is better than those of the backpropagation NN (BPNN) system, the hard c-means (HCM) and parallel NNs system, and the pattern-matching system     

The theoretical analysis of GLRAM and its applications

Matrix-based methods such as two-dimensional principal component analysis (2DPCA) and generalized low rank approximations of matrices (GLRAM) have gained wide attention from researchers due to their computational efficiency. In this paper, we propose a non-iterative algorithm for GLRAM. Firstly, the optimal property of GLRAM is revealed, which is closely related to PCA. Moreover, it also shows that the reconstruction error of GLRAM is not smaller than that of PCA when considering the same dimensionality. Secondly, a non-iterative algorithm for GLRAM is derived. And the proposed method obtains smaller reconstruction error than 2DPCA or GLRAM. Finally, experimental results on face images and handwritten numeral characters show that the proposed method can achieve competitive results with some existing methods such as 2DPCA and PCA in terms of the classification performance or the reconstruction error.     

Neural networks for advanced control of robot manipulators

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.     

Neural networks stabilization and disturbance attenuation for nonlinear switched impulsive systems

In this paper, we address the problem of neural networks (NNs) stabilization and disturbance rejection for a class of nonlinear switched impulsive systems. An adaptive NN feedback control scheme and an impulsive controller for output tracking error disturbance attenuation of nonlinear switched impulsive systems are given under all admissible switched strategy based on NN. The NN is used to compensate for the nonlinear uncertainties of switched impulsive systems, and the approximation error of NN is introduced to the adaptive law in order to improve the tracking attenuation quality of the switched impulsive systems. Impulsive controller is designed to attenuate effect of switching impulse. Under all admissible switching law, impulsive controller and adaptive NN feedback controller can guarantee asymptotic stability of tracking error and improve disturbance attenuation level of tracking error for the overall nonlinear switched impulsive system. Finally, a numerical example is given to demonstrate the effectiveness of the proposed control and stabilization methods.     

模块二维主成分分析——人脸识别新方法

提出了模块二维主成分分析(M2DPCA)线性鉴别分析方法。M2DPCA方法先对图像矩阵进行分块，对分块得到的子图像矩阵直接进行鉴别分析。其特点是：能有效地降低模式原始特征的维数；可以完全避免使用矩阵的奇异值分解，特征抽取方便；此外，2DPCA是M2DPCA的特例。在ORL人脸库上试验结果表明，M2DPCA方法在识别性能上优于PCA，比2DPCA更具有鲁棒性。     

Adaptive neural network control of nonlinear systems by state andoutput feedback

This paper presents a novel control method for a general class of nonlinear systems using neural networks (NNs). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a high-gain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions.     

二维主元分析在人脸识别中的应用研究

结合二维主元分析（two-dimensional principal component analysis,2DPCA）的特点,将2DPCA算法用于人脸识别。它与主元分析（principal component analysis,PCA）的不同之处在于,2DPCA算法以图像矩阵为分析对象;而PCA算法以图像的一维向量为分析对象。2DPCA算法是直接利用原始图像矩阵构造图像的协方差矩阵。而PCA算法需对原始图像矩阵先降维、再将降维矩阵转换成列向量,然后构造图像的协方差矩阵。为了测试和评估2DPCA算法的性能,在ORL（olivetti research laboratory）与Yale人脸数据库上进行了实验,结果表明,2DPCA算法用于人脸识别的正确识别率高于PCA算法。同时,也显示了2DPCA算法在特征提取方面比PCA算法更有效。     

基于子模式的完全二维主成分分析的步态识别算法

提出一种基于子模式的完全二维主成分分析的步态识别算法.首先对步态能量图进行子块划分,自适应地去掉对分类无用的子块.然后分别对每个子图像采用完全二维主成分分析方法进行特征抽取.最后将各个子块的特征合为整体采用最近邻分类器来测试识别.应用上述方法在CASIA步态数据库上进行实验,通过实验确定分块数目.实验结果表明本文算法明显好于完全二维主成分分析方法,不但有利于提取局部特征,而且对外套变化、背包,行走方向变化的步态识别也较有效.     

Diagonal principal component analysis for face recognition

In this paper, a novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition. In contrast to standard PCA, DiaPCA directly seeks the optimal projective vectors from diagonal face images without image-to-vector transformation. While in contrast to 2DPCA, DiaPCA reserves the correlations between variations of rows and those of columns of images. Experiments show that DiaPCA is much more accurate than both PCA and 2DPCA. Furthermore, it is shown that the accuracy can be further improved by combining DiaPCA with 2DPCA.     

Adaptive backstepping control for a class of nonlinear systems using neural network approximations

In this paper, an adaptive neural network (NN) backstepping technique is developed for tracking control of a class of nonlinear systems. NNs are used to compensate for the unknown nonlinear functions in the system. A systematic backstepping approach is established to synthesize the adaptive NN control scheme that ensures the boundedness of all the signals in the closed‐loop system, and yields a small tracking error. The issue of transient performance is also addressed under an analytical framework. The effectiveness of the proposed scheme is demonstrated by computer simulations. Copyright © 2004 John Wiley & Sons, Ltd.     

Composite adaptive locally weighted learning control for multi-constraint nonlinear systems

A composite adaptive locally weighted learning (LWL) control approach is proposed for a class of uncertain nonlinear systems with system constraints, including state constraints and asymmetric control saturation in this paper. The system constraints are tackled by considering the control input as an extended state variable and introducing barrier Lyapunov functions (BLFs) into the backstepping procedure. The system uncertainty is approximated by a composite adaptive LWL neural networks (NNs), in which a prediction error is constructed by using a series-parallel identification model, and NN weights are updated by both the tracking error and the prediction error. The update law with composite error feedback improves uncertainty approximation accuracy and trajectory tracking accuracy. The feasibility and effectiveness of the proposed approach have been demonstrated by formal proof and simulation results.     

二维投影非负矩阵分解算法及其在人脸识别中的应用

建立在最小化非负矩阵分解损失函数上的人脸识别算法需同时计算基矩阵和系数矩阵, 导致求解这类问题十分耗时. 本文把非负属性引入二维主成分分析(2-dimensional principal component analysis, 2DPCA)中, 提出了一种新的二维投影非负矩阵分解(2-dimensional projective non-negative matrix factorization, 2DPNMF)人脸识别算法. 该算法在保持人脸图像的局部结构情况下, 突破了最小化非负矩阵分解损失函数的约束, 仅需计算投影矩阵(基矩阵), 从而降低了计算复杂度. 本文从理论上证明了所提出算法的收敛性, 同时, 使用了YALE、FERET和AR三个人脸库进行实验, 结果表明2DPNMF不仅识别率高, 而且速度优于非负矩阵分解和二维主成分分析.     

基于PCA和LDA结合的人脸识别方法的比较研究

针对用于人脸识别的主分量分析(PCA)计算量大、识别效果不佳和分类时间长,线性判别分析(LDA)存在小样本问题,比较研究几种基于PCA和LDA的人脸识别方法,这些方法包括PCA+ LDA、2DPCA+ DLDA和2DPCA+2DLDA.在理论和实验上比较研究这些方法,且在ORL和Yale人脸库的实验数据表明,结合后的方法比结合前的方法分类效果好,分类时间短,且在这几种方法中,2DPCA+2DLDA的效果最佳.     

Robust backstepping control of nonlinear systems using neuralnetworks

A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed, so no preliminary dynamical analysis is needed. One salient feature of our NN approach is that there is no need for the off-line learning phase. Three nonlinear systems, including a one-link robot, an induction motor, and a rigid-link flexible-joint robot, were used to demonstrate the effectiveness of the proposed scheme     

基于二维主分量分析的面部表情识别

提出了一种直接基于图像矩阵的二维主分量分析(2DPCA)和多分类器联合的面部表情识别方法。首先利用2DPCA进行特征提取,然后用基于模糊积分的多分类器联合的方法对七种表情(生气、厌恶、恐惧、高兴、中性、悲伤、惊讶)进行识别。在JAFFE人脸表情静态图像库上进行实验,与传统主分量分析(PCA)相比,采用2DPCA进行特征提取,不仅识别率比较高,而且运算速度也有很大的提高。     

Neural-Network-Based State Feedback Control of a Nonlinear Discrete-Time System in Nonstrict Feedback Form

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.