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Improved nonlinear PCA for process monitoring using support vector data description

JOURNAL OF PROCESS CONTROL 2011年第9期21卷 1306-1317页

作者： Liu, Xueqin Li, Kang McAfee, Marion Irwin, George W. Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Intelligent Syst & Control Grp Belfast BT9 5AH Antrim North Ireland Inst Technol Sligo Dept Mech & Elect Engn Sligo Ireland

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the fast recursive algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Nonlinear principal component analysis Support vector datadescription fast recursive algorithm Principal curves Radial basis function network

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An Effective Method of Threshold Selection for Small Object Image

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Defence Technology（防务技术） 2011年第4期7卷 235-242页

作者：吴一全吴加明占必超 School of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Science and Technology on Electro-optic Control Laboratory Institute of Electro-optic Equipment of AVIC State Key Laboratory of Novel Software Technology Nanjing University

The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.

关键词： information processing small infrared target detection image segmentation threshold selection 2-D histogram oblique segmentation fast recursive algorithm

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Two-stage extreme learning machine for regression

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NEUROCOMPUTING 2010年第16-18期73卷 3028-3038页

作者： Lan, Yuan Soh, Yeng Chai Huang, Guang-Bin Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Extreme learning machine (ELM) proposed by Huang et al. was developed for generalized single hidden layer feedforward networks (SLFNs) with a wide variety of hidden nodes. It proved to be very fast and effective especially for solving function approximation problems with a predetermined network structure. However, the method for determining the network structure of preliminary ELM may be tedious and may not lead to a parsimonious solution. In this paper, a systematic two-stage algorithm (named TS-ELM) is introduced to handle the problem. In the first stage, a forward recursive algorithm is applied to select the hidden nodes from the candidates randomly generated in each step and add them to the network until the stopping criterion achieves its minimum. The significance of each hidden node is then reviewed in the second stage and the insignificance ones are removed from the network, which drastically reduces the network complexity. The effectiveness of TS-ELM is verified by the empirical studies in this paper. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Extreme learning machine fast recursive algorithm Final prediction error Leave-one-out cross-validation

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On fast recursive algorithms for V-BLAST with Optimal Ordered SIC Detection

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2009年第6期8卷 2860-2865页

作者： Shang, Yue Xia, Xiang-Gen Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

A fast recursive algorithm for the vertical Bell Laboratories layered space-time (V-BLAST) with the optimal ordered successive interference cancellation (SIC) detection has been proposed by Benesty-Huang-Chen and two improved algorithms have been also recently introduced by Szczecinski-Massicotte and Zhu-Lei-Chin independently. In this letter, our first contribution is to incorporate the existing improvements into the original fast recursive algorithm to provide the fastest known algorithm for the optimal ordered SIC detection of V-BLAST. Subsequently, we propose two new algorithms that result from one further improvement for the fast recursive algorithm. Compared with the fastest known algorithm built from the existing improvements, one new proposed algorithm has a speedup of 1.3 times in both the number of multiplications and the number of additions, and the other new proposed algorithm requires less intermediate variables and saves memories.

关键词： fast recursive algorithm MMSE successive interference cancellation V-BLAST ZF

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A high-efficiency digital image correlation method based on a fast recursive scheme

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MEASUREMENT SCIENCE AND TECHNOLOGY 2010年第3期21卷 035101-035101页

作者： Huang, Jianyong Zhu, Tao Pan, Xiaochang Qin, Lei Peng, Xiaoling Xiong, Chunyang Fang, Jing Peking Univ Dept Biomed Engn Beijing 100871 Peoples R China Peking Univ Acad Adv Interdisciplinary Studies Beijing 100871 Peoples R China Peking Univ Third Hosp Inst Vasc Med Beijing 100083 Peoples R China

Due to computational complexity of correlation searching, the digital image correlation (DIC) method is often extremely time consuming in image processing and optical measurement, which has limited its further applications to a great extent. This paper develops a fast recursive scheme to mathematically reduce the computational burden of the traditional DIC technique and therefore improve its efficiency in deformation calculation. A global sum-table approach is proposed to simplify the computations of all double sums arising in the zero-normalized cross-correlation coefficient (ZNCC). A fast recursive algorithm is established to accelerate the calculation of the cross-correlation term in the ZNCC. Both theoretical analysis and actual displacement acquisition are carried out to validate the performance of the new DIC algorithm, which indicates that the fast recursive scheme can improve computational efficiency of integer-pixel correlation searching by about 10 to 50 times in comparison with the classic DIC algorithm, on the condition of keeping the measurement accuracy.

关键词： zero-normalized cross correlation sum tables fast recursive algorithm cell-substrate displacement measurement

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A fast model identification method for networked control system

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APPLIED MATHEMATICS AND COMPUTATION 2008年第2期205卷 658-667页

作者： Fei, Minrui Du, Dajun Li, Kang Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5AH Antrim North Ireland

Most control strategies for networked control system (NCS) assume that system model is known as 'a priori', which are however impractical in many industrial applications. To obtain a suitable model for networked control, this paper concentrates on model identification under the networked control environment. A networked identification scheme is proposed here, and nondeterministic factors such as network-induced delay, data packet out-of-order, and data packet loss between the sensor and identifier as well as the identifier and the actuator are considered. A discard-packet strategy is first developed to deal with network nondeterministic factors from the sensor to the identifier, and a cubic spline interpolation is used to compensate lost data. Actuator buffers are then introduced for actuator nodes to handle network nondeterministic factors between the identifier and the actuator. Finally, a fast recursive algorithm is used both to select the model structure and to estimate the unknown system parameters. Experiments on ARMA model identification and NARMAX model identification are performed respectively under different network loads. Simulation results show that the proposed networked identification scheme can effectively overcome the impact of various network-induced nondeterministic factors and significantly improve model identification performance. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Networked control system Network-induced delay fast recursive algorithm Cubic spline

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A fast nonlinear model identification method

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2005年第8期50卷 1211-1216页

作者： Li, K Peng, JX Irwin, GW Queens Univ Belfast Sch Elect & Elect Engn Belfast BT9 5AH Antrim North Ireland

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

关键词： computational complexity fast recursive algorithm nonlinear system identification numerical stability

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Structure-adaptive image filtering using trimmed mean filters 8

Structure-adaptive image filtering using trimmed mean filter...

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Conference on Nonlinear Image Processing VIII

作者： Palenichka, RM Zinterhof, P National Academy of Sciences of Ukraine (Ukraine) Univ. Salzburg (Austria)

ISBN: (纸本)0819424374

In this paper, a structure-adaptive approach to the nonlinear image filtering is described. The adaptive procedure is based on selection of the most homogenous neighborhood region from several possible structuring regions by the principle of maximum a posteriori probability. Then, an optimal evaluation of the pixel value is performed involving pixels from the determined neighborhood region. Trimmed mean filters are used for the robust evaluation of local properties during estimation of object and background intensities when the noise has a mixed conditional distribution, e.g. normal distribution with outliers.

关键词： structure-adaptive filtering trimmed mean mathematical morphology structuring element structuring region fast recursive algorithm

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APPROXIMATION OF MULTIVARIABLE LINEAR-SYSTEMS WITH IMPULSE-RESPONSE AND AUTO-CORRELATION SEQUENCES

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AUTOMATICA 1983年第3期19卷 265-277页

作者： INOUYE, Y Department of Control Engineering Faculty of Engineering Science Osaka University Toyonaka Osaka 60 Japan

This paper considers the construction of approximants of multi-input-multi-output, discrete-time linear systems from the finite data of the impulse response and autocorrelation sequences. In the approximation of a multivariable linear system, it is common practice to use a finite portion of its impulse response sequence. This is formally equivalent to the Padé approximation technique, which may produce unstable approximants, even though the original system is stable. Mullis and Roberts proposed a new method, which yields stable approximants, in connection with approximation of digital filters. This is, however, restricted to the single-input-single-output case. This paper extends their method to the multi-input-multi-output case and shows a fast recursive algorithm to construct stable approximants of linear systems.

关键词： correlation method fast recursive algorithm interpolation problem least-squares approximation Modelling multivariable systems sampled data systems

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Approximation of Multivariable Linear Systems with Impulse Response and Autocorrelatlon Sequences

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IFAC Proceedings Volumes 1981年第2期14卷 1-6页

作者： Y. Inouye Department of Control Engineering Faculty of Engineering Science Osaka University Toyonaka Osaka 560 japa

This paper considers the construction of approximants of multi-input-output, discrete-time linear systems from the finite data of the impluse response and autocorrelation sequences (or cross correlation between input and output, and autocorrelation of output). In the approximation of multivariable linear systems, it is common practice to use the impluse response sequence This is formally equivalent to the Padé approximation technique, which may produce unstable approximants, even though the original system is stable. Mullis and Roberts proposed a new method, which yields stable appr0ximants, in connection with the approximation of digital filters. This is, however, restricted to the single input-output case. This paper extends their method to the multi-input-output case and shows a fast recursive algorithm to construct stable approximants of linear systems

关键词： Modelling multivariable systems correlation methods least squares approximation interpolation problem sampled data systems fast recursive algorithm

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