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2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Baklouti, Raoudha Mansouri, Majdi Nounou, Mohamed Ben Messaoud, Zaineb Ben Hamida, Ahmed Sfax Univ ENIS ATMS Sfax Tunisia Texas A&M Univ Qatar Doha Qatar

ISBN: (纸本)9781467385268

By applying generalized hebbian algorithm (GHA), this work deals with the problem of on-line process monitoring for a continuously stirred tank reactor (CSTR) model using Principal Component Analysis (PCA) method. Diverse studies have shown the efficiency of PCA for fault detection. However, this method for a large number of samples, becomes difficult to directly solve the eigenvalue problem especially with a large number of samples, which make it not suitable in the cases of on-line process monitoring. In this paper, Iterated PCA have been proposed to alleviate the impact of this problem. This method uses GHA for optimizing the memory efficiency. The simulation results show the effectiveness of the IPCA method in terms of fault detection accuracies, false alarm rates for detection of single as well as multiple sensor faults through its two charts Q and Hotelling T-2 statistics.

关键词： generalized hebbian algorithm iterated Principal Component Analysis Continuously Stirred Tank Reactor Fault Detection

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Compact Optimization Learning for AC Optimal Power Flow

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第2期39卷 4350-4359页

作者： Park, Seonho Chen, Wenbo Mak, Terrence W. K. Van Hentenryck, Pascal Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

This article reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability issues due to the high dimensionality of the output space. This article first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA). It then proposes COMPACT LEARNING, a new method that learns in a subspace of the principal components and translates the vectors into the original output space. This compression reduces the number of trainable parameters substantially, improving scalability and effectiveness. COMPACT LEARNING is evaluated on a variety of test cases from the PGLib and a realistic French transmission system having renewable energy changes with up to 30,000 buses. The article also shows that the output of COMPACT LEARNING can be used to warm-start an exactACsolver to restore feasibility, while bringing significant speed-ups.

关键词： Deep learning end-to-end learning generalized hebbian algorithm nonlinear programming optimal power flow principal component analysis

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Compressed Sensing with generalized hebbian algorithm in Video Frame Prediction

Compressed Sensing with Generalized Hebbian Algorithm in Vid...

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3rd International Conference on Advanced Design and Manufacturing Engineering (ADME 2013)

作者： Gu, Mingming Jing, Qi Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China

ISBN: (纸本)9783037858431

Compressed Sensing with generalized hebbian algorithm (GHA) in Video Frame Prediction is proposed in the paper. After analyzing the inter-frame correlation among the images of video sequences, GHA, as neural network algorithm of PCA, is adopted to remove the transform coefficients with lower value according in order to implement video compressed sensing. Furthermore, for statistics of the adjacent frames are similar enough, the algorithm processes superiority in video frame prediction. Simulation results show that, the proposed algorithm can not only improve the reconstructed quality and the visual effects of the video sequence, but also save the sampling resources. Moreover, video frames can be predicted capitally through the application of the algorithm.

关键词： Principal Component Analysis Compressed Sensing Video Frame Prediction Eigenvector generalized hebbian algorithm

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Classical vs. neural network-based PCA approaches for lossy image compression: Similarities and differences

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APPLIED SOFT COMPUTING 2024年 161卷

作者： Bartecki, Krzysztof Opole Univ Technol Div Control Sci & Engn ul Proszkowska 76 PL-45758 Opole Poland

The paper describes three lossy data compression techniques based on the principal component analysis (PCA), which are compared using the image compression task. The presented approach uses both classical PCA method based on the eigen-decomposition of the image data covariance matrix and two different neural network structures. The first neural structure is a two-layer feed-forward network with supervised learning acting as the so-called autoencoder, and the second one is a single-layered network with an unsupervised learning method commonly known as the generalized hebbian algorithm. For each compression method mentioned above, the influence of the number of image segments (frames) and the number of eigenvalues/eigenvectors on the compression ratio and the image quality are examined using three different gray-scale test images. The paper also addresses a vital implementation issue regarding selecting appropriate data types to represent the compressed data. The paper's main conclusion is that the classical PCA method outperforms its neural counterparts, both in terms of the decompressed image quality and the time required to perform the compression procedure. The positive aspect of using neural networks as a tool for PCA-based lossy data compression is that they do not require calculating the correlation matrix explicitly and thus can be used in online data acquisition schemes.

关键词： Artificial neural network Autoencoder generalized hebbian algorithm Lossy image compression Principal component analysis

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FPGA Implementation of generalized hebbian algorithm for Texture Classification

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SENSORS 2012年第5期12卷 6244-6268页

作者： Lin, Shiow-Jyu Hwang, Wen-Jyi Lee, Wei-Hao Natl Taiwan Normal Univ Dept Comp Sci & Informat Engn Taipei 116 Taiwan Natl Ilan Univ Dept Elect Engn Yilan 260 Taiwan

This paper presents a novel hardware architecture for principal component analysis. The architecture is based on the generalized hebbian algorithm (GHA) because of its simplicity and effectiveness. The architecture is separated into three portions: the weight vector updating unit, the principal computation unit and the memory unit. In the weight vector updating unit, the computation of different synaptic weight vectors shares the same circuit for reducing the area costs. To show the effectiveness of the circuit, a texture classification system based on the proposed architecture is physically implemented by Field Programmable Gate Array (FPGA). It is embedded in a System-On-Programmable-Chip (SOPC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs.

关键词： system on programmable chip reconfigurable computing principal component analysis generalized hebbian algorithm texture classification FPGA

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Efficient hardware architecture based on generalized hebbian algorithm for texture classification

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NEUROCOMPUTING 2011年第17期74卷 3248-3256页

作者： Lin, Shiow-Jyu Hung, Yi-Tsan Hwang, Wen-Jyi Natl Taiwan Normal Univ Dept Comp Sci & Informat Engn Taipei 116 Taiwan Natl Ilan Univ Dept Elect Engn Yilan 260 Taiwan

The objective of this paper is to present an efficient hardware architecture for generalized hebbian algorithm (GHA). In the architecture, the principal component computation and weight vector updating of the GHA are operated in parallel, so that the throughput of the circuit can be significantly enhanced. In addition, the weight vector updating process is separated into a number of stages for lowering area costs and increasing computational speed. To show the effectiveness of the circuit, a texture classification system based on the proposed architecture is designed. It is embedded in a system-on-programmable-chip (SOPC) platform for physical performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs. (C) 2011 Elsevier B.V. All rights reserved.

关键词： generalized hebbian algorithm Principal component analysis System-on-programmable-chip

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A recursive least square algorithm for online kernel principal component extraction

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NEUROCOMPUTING 2017年 237卷 255-264页

作者： Souza Filho, Joao B. O. Diniz, Paulo S. R. Univ Fed Rio de Janeiro Polytech Sch Dept Elect & Comp Engn Technol Ctr Ave Athos Silveira Ramos 149Bldg H2nd Floor Rio De Janeiro Brazil Fed Ctr Technol Educ Celso Suckow Fonseca Elect Engn Postgrad Program PPEEL Ave Maracana 229Bldg E5th Floor Rio De Janeiro Brazil Univ Fed Rio de Janeiro Alberto Luiz Coimbra Inst COPPE Elect Engn Program PEE Rio De Janeiro Brazil

The online extraction of kernel principal components has gained increased attention, and several algorithms proposed recently explore kernelized versions of the generalized hebbian algorithm (GHA) [1], a well-known principal component analysis (PCA) extraction rule. Corisequently, the convergence speed of such algorithms and the accuracy of the extracted components are highly dependent on a proper choice of the learning rate, a problem dependent factor. This paper proposes a new online fixed-point kerriel principal component extraction algorithm, exploring the minimization of a recursive least-square error function, conjugated with an approximated deflation transform using component estimates obtained by the algorithm, implicitly applied upon data. The proposed technique automatically builds a concise dictionary to expand kernel components, involves simple recursive equations to dynamically define a specific learning rate to each component under extraction, and has a linear computational complexity regarding dictionary size. As compared to state-of-art kernel principal component extraction algorithms, results show improved convergence speed and accuracy of the components produced by the proposed method in five open-access databases.

关键词： Kernel principal components analysis Kernel methods Online kernel algorithms Machine learning generalized hebbian algorithm

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A Fixed-Point Online Kernel Principal Component Extraction algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2017年第23期65卷 6244-6259页

作者： Souza Filho, Joao B. O. Diniz, Paulo S. R. Univ Fed Rio de Janeiro Dept Elect & Comp Engn BR-68504 Rio De Janeiro Brazil

Kernel principal component analysis (KPCA) is a powerful and widely applied nonlinear feature extraction technique. However, as originally proposed, KPCA may be cumbersome or infeasible in large-scale datasets, which motivated the development of low-complexity iterative extraction algorithms, mainly aiming image processing applications. Recently, some online KPCA extraction algorithms were proposed, but most of them suffer from low-convergence speed. This paper proposes a new algorithm based on fixed-point iterative equations for KPCA extraction, expanding kernel components using a compact dictionary, dynamically built from data, according to a user-defined accuracy parameter. The algorithm relies on simple equations, can track nonstationary environments, and requires reduced storage, enabling its use in real-time applications operating in low-cost embedded hardware. Results involving open-access image datasets show improved accuracy and convergence speed, as well as permitted effective improvements in practical image applications, as compared to state-of-art online KPCA techniques.

关键词： Principal component analysis kernel methods online kernel algorithms machine learning generalized hebbian algorithm

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Efficient Architecture for Spike Sorting in Reconfigurable Hardware

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SENSORS 2013年第11期13卷 14860-14887页

作者： Hwang, Wen-Jyi Lee, Wei-Hao Lin, Shiow-Jyu Lai, Sheng-Ying Natl Taiwan Normal Univ Dept Comp Sci & Informat Engn Taipei 116 Taiwan Natl Ilan Univ Dept Elect Engn Yilan 260 Taiwan

This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation.

关键词： spike sorting reconfigurable computing system-on-chip generalized hebbian algorithm fuzzy C-means FPGA

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Image compression using PCA with clustering

Image compression using PCA with clustering

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IEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS)

作者： Wang, Chih-Wen Jeng, Jyh-Horng I Shou Univ Dept Inform Eng Kaohsiung Taiwan

ISBN: (纸本)9781467350839

Principal component analysis (PCA), a statistical processing technique, transforms the data set into a lower dimensional feature space, yet retain most of the intrinsic information content of the original data. In this paper, we apply PCA for image compression. In the PCA computation, we adopt the neural network architecture in which the synaptic weights, served as the principal components, are trained through generalized hebbian algorithm (GHA). Moreover, we partition the training set into clusters using K-means method in order to obtain better retrieved image qualities.

关键词： Image compression K-means algorithm Principal component analysis generalized hebbian algorithm

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