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19th International Symposium on Power Electronics (Ee)

作者： Kusljevic, Miodrag D. ' Tomic, Josif J. Poljak, Predrag D. Termoelektro Enel AD Uralska 9 Belgrade 11060 Serbia Univ Novi Sad Fac Tech Sci Trg Dositeja Obradov 6 Novi Sad 21000 Serbia Inst Chem Technol & Met Dept Microelect Technol Njegoseva 12 Beograd 11000 Serbia

ISBN: (纸本)9781538635025

In this paper, a modified and improved approach to the recently proposed multiple-resonator-based observer structure for harmonic estimation has been proposed. In the previous papers, two inherent particular cases have been considered. In the first case, estimation is performed in the point located in the middle of the observation interval, and exhibits good noises and unwanted harmonics attenuation but possesses a large response delay. In the second case, the estimation point is at the end of the observation window. In this case, the filters are able to form a zero-flat phase response about the operation frequency and hence able to provide instantaneous estimates, but with large overshoots caused by resonant frequencies at the edges of the pass band, and the high level of the sidelobs. In this paper, the estimation point is shifted along the observation interval reshaping frequency responses to tradeoff between those opposite requirements. The effectiveness of the proposed algorithm is shown through simulations.

关键词： Discrete Fourier transform (DFT) group delay harmonic analysis finite-impulse-response (FIR) filter maximally flat (MF) filter multiple-resonator (MR) recursive algorithm Taylor-Fourier transform (TFT)

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High Performance recursive LU Factorization for Multicore Systems 14

High Performance Recursive LU Factorization for Multicore Sy...

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14th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)

作者： Mahfoudhi, Ryma Univ Tunis El Manar Fac Sci Tunis Algorithm Parallele & Optimisat UR13ES38 Tunis 2092 Tunisia

ISBN: (纸本)9781538635810

The LU factorization (LUF) algorithm is an important kernel used in many Linear Algebra applications such as the resolution of systems of linear equations, the inversion of square matrices and the computation of matrix eigenvalues. It is also used in the LINPACK benchmark for ranking the Top 500 most powerful supercomputers. We propose in this paper a parallel recursive algorithm for LUF based on the 'Divide and Conquer' paradigm. A theoretical performance study permits to establish an accurate comparison between the designed algorithm and the PBLAS library. We achieved a series of experiments that permits to validate the contribution and lead to efficient performances obtained for large sized matrices i.e. up to 40% faster than SCALAPACK.

关键词： Divide and Conquer LU factorization Multicore architecture Parallel algorithm PBLAS recursive algorithm

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recursive algorithm for L1 Norm Estimation in Linear Models

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JOURNAL OF SURVEYING ENGINEERING 2011年第1期137卷 1-8页

作者： Khodabandeh, A. Amiri-Simkooei, A. R. Univ Tehran Dept Surveying & Geomat Engn Geodesy Div Fac Engn Tehran Iran Delft Univ Technol Delft Inst Earth Observat & Space Syst Fac Aerosp Engn NL-2629 HS Delft Netherlands Univ Isfahan Dept Surveying Engn Fac Engn Esfahan 81744 Iran

L1 norm estimator has been widely used as a robust parameter estimation method for outlier detection. Different algorithms have been applied for L1 norm minimization among which the linear programming problem based on the simplex method is well known. In the present contribution, in order to solve an L1 norm minimization problem in a linear model, an interior point algorithm is developed which is based on Dikin's method. The method can be considered as an appropriate alternative for the classical simplex method, which is sometimes time-consuming. The proposed method, compared with the simplex method, is thus easier for implementation and faster in performance. Furthermore, a recursive form of the Dikin's method is derived, which resembles the recursive least-squares method. Two simulated numerical examples show that the proposed algorithm gives as accurate results as the simplex method but in considerably less time. When dealing with a large number of observations, this algorithm can thus be used instead of the iteratively reweighted least-squares method and the simplex method.

关键词： Iteratively reweighted least squares Linear programming problem Interior point methods Simplex method Affine method of Dikin Robust estimation L1 norm minimization recursive algorithm

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recursive algorithms for parameter estimation with adaptive quantizer

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AUTOMATICA 2015年 52卷 192-201页

作者： You, Keyou Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

This paper studies a parameter estimation problem of networked linear systems with fixed-rate quantization. Under the minimum mean square error criterion, we propose a recursive estimator of stochastic approximation type, and derive a necessary and sufficient condition for its asymptotic unbiasedness. This motivates to design an adaptive quantizer for the estimator whose strong consistency, asymptotic unbiasedness, and asymptotic normality are rigorously proved. Using the Newton-based and averaging techniques, we obtain two accelerated recursive estimators with the fastest convergence speed of O(1/k), and exactly evaluate the quantization effect on the estimation accuracy. If the observation noise is Gaussian, an optimal quantizer and the accelerated estimators are co-designed to asymptotically approach the minimum Cramer-Rao lower bound. All the estimators share almost the same computational complexity as the gradient algorithms with un-quantized observations, and can be easily implemented. Finally, the theoretical results are validated by simulations. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Parameter estimation recursive algorithm Quantization Asymptotic analysis Convergence speed

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A recursive deterministic algorithm for treatment of true coincidence summing effects in gamma-ray spectrometry

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APPLIED RADIATION AND ISOTOPES 2012年第4期70卷 726-732页

作者： Vidmar, Tim Kanisch, Guenter Belgian Nucl Res Ctr SCK CEN B-2400 Mol Belgium Inst Fishery Ecol Johann Heinrich von Thunen Inst D-20539 Hamburg Germany

Different algorithms exist that can be applied to the calculation of the effects of true coincidence summing in gamma-ray spectrometry. Some of these, however, are not capable of reproducing the count rates in all the pure sum peaks that a spectrum may contain. A recursive, easy-to-implement deterministic algorithm has been developed that overcomes this shortcoming. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： Gamma-ray spectrometry True coincidence summing effects Deterministic recursive algorithm

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recursive algorithm FOR PARITY GAMES REQUIRES EXPONENTIAL TIME

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RAIRO-THEORETICAL INFORMATICS AND APPLICATIONS 2011年第4期45卷 449-457页

作者： Friedmann, Oliver LMU Munchen Inst Informat D-80538 Munich Germany

This paper presents a new lower bound for the recursive algorithm for solving parity games which is induced by the constructive proof of memoryless determinacy by Zielonka. We outline a family of games of linear size ... 详细信息

关键词： Parity games recursive algorithm lower bound mu calculus model checking

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recursive modular modelling methodology for lumped-parameter dynamic systems

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PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 2017年第2204期473卷 20160891-20160891页

作者： Matarazzo Orsino, Renato Maia Univ Sao Paulo Escola Politecn Dept Mech Engn Offshore Mech Lab Sao Paulo Brazil

This paper proposes a novel approach to the modelling of lumped-parameter dynamic systems, based on representing them by hierarchies of mathematical models of increasing complexity instead of a single (complex) model. Exploring the multilevel modularity that these systems typically exhibit, a general recursive modelling methodology is proposed, in order to conciliate the use of the already existing modelling techniques. The general algorithm is based on a fundamental theorem that states the conditions for computing projection operators recursively. Three procedures for these computations are discussed: orthonormalization, use of orthogonal complements and use of generalized inverses. The novel methodology is also applied for the development of a recursive algorithm based on the Udwadia-Kalaba equation, which proves to be identical to the one of a Kalman filter for estimating the state of a static process, given a sequence of noiseless measurements representing the constraints that must be satisfied by the system.

关键词： mathematical modelling modular modelling recursive algorithm dynamic systems multibody systems Kalman filter

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recursive Updating algorithm for Robust Capon Beamforming with Steering Vector Mismatches

Recursive Updating Algorithm for Robust Capon Beamforming wi...

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46th Asilomar Conference on Signals, Systems and Computers

作者： Mavrychev, Evgeny A. Nizhnii Novgorod State Tech Univ Dept Informat Radio Syst Nizhnii Novgorod Russia

ISBN: (纸本)9781467350518

A new algorithm for robust adaptive beamforming is designed in this paper. It is assumed that steering vector is mismatches due to propagation effects, array calibration errors, etc. The proposed algorithm is based on recursive updating of sample covariance matrix and adaptive diagonal loading. New implementation of robust Capon beam former (RCB) is more robustness to non-stationary interference environment. Simulation results confirm efficiency of recursive robust adaptive beamformer.

关键词： sensor array adaptive beamformer robust processing recursive algorithm

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Minimum-Variance recursive Filtering Over Sensor Networks With Stochastic Sensor Gain Degradation: algorithms and Performance Analysis

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2016年第3期3卷 265-274页

作者： Liu, Yang Wang, Zidong He, Xiao Zhou, D. H. Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China

This paper is concerned with the minimum variance filtering problem for a class of time-varying systems with both additive and multiplicative stochastic noises through a sensor network with a given topology. The measurements collected via the sensor network are subject to stochastic sensor gain degradation, and the gain degradation phenomenon for each individual sensor occurs in a random way governed by a random variable distributed over the interval [0, 1]. The purpose of the addressed problem is to design a distributed filter for each sensor such that the overall estimation error variance is minimized at each time step via a novel recursive algorithm. By solving a set of Riccati-like matrix equations, the parameters of the desired filters are calculated recursively. The performance of the designed filters is analyzed in terms of the boundedness and monotonicity. Specifically, sufficient conditions are obtained under which the estimation error is exponentially bounded in mean square. Moreover, the monotonicity property for the error variance with respect to the sensor gain degradation is thoroughly discussed. Numerical simulations are exploited to illustrate the effectiveness of the proposed filtering algorithm and the performance of the developed filter.

关键词： Error boundedness minimum variance filtering monotonicity recursive algorithm sensor gain sensor network

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Hybrid neural network predictor for distributed parameter system based on nonlinear dimension reduction

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NEUROCOMPUTING 2016年 171卷 1591-1597页

作者： Wang, Mengling Qi, Chenkun Yan, Huaicheng Shi, Hongbo E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200030 Peoples R China

In this study, a hybrid neural network predictor is proposed to predict spatiotemporal dynamics of the nonlinear distributed parameter systems (DPSs) with unwanted disturbance or slow set point changes. First, a nonlinear principal component analysis (NL-PCA) network is designed to transform the high-dimensional spatiotemporal data into a low-dimensional time domain, which can better represent the nonlinearity of the system compared to the linear time/space separation method. Then the hybrid NN models are built to identify the low-dimensional temporal data. To capture the spatiotemporal dynamics of DPS, the four-step recursive algorithm is used to obtain the time-varying weights of the model, while the parameters of NN model does not need to online update. The simulations demonstrated show that the proposed approach can achieve a good performance on prediction with system slow time-varying dynamics. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Neural network Nonlinear parabolic distributed parameter system Nonlinear dimension reduction recursive algorithm

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