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Sparse optimization and multi-performance enhancement of a high frequency to very high frequency wideband radio array using a genetic algorithm 认领 引用
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作者 Yifeng Qin Jiarui Di +8 位作者 Liang Dong Zhang Ning Yan Liu Lesheng He Abidin Zamri Zainal Huanhuan Xie Latef Abdul Tarik Wei He Othman Mohamadariff 《Astronomical Techniques and Instruments》 CSCD 2026年第3期235-249,共15页
The High Frequency(HF, 3–30 MHz) to Very High Frequency(VHF, 30–300 MHz) band is a critical observational window in radio astronomy, playing a key role in the study of early-universe reionization, space weather moni... The High Frequency(HF, 3–30 MHz) to Very High Frequency(VHF, 30–300 MHz) band is a critical observational window in radio astronomy, playing a key role in the study of early-universe reionization, space weather monitoring, and solar physics. We determine whether a genetic algorithm-optimized sparse configuration of a 64-element planar radio antenna array can minimize the peak sidelobe level and enhance performance within the 10–90 MHz frequency range, compared with a regular configuration. The sparse-optimized array achieves a 1.04 dB reduction in peak sidelobe level across the frequency band compared with the regular array. Sensitivity improves significantly at all frequency points, with increases of up to 56% at 10 MHz and 45% at 50 MHz. At 90 MHz, the sensitivity matches that of the regular array. At three representative frequencies(50 MHz, 60 MHz, and 70 MHz), grating lobe suppression tests at different scan angles show that the regular array shows prominent grating lobes at specific scan angles(θ =53° at 50 MHz, θ = 30° at 60 MHz, and θ = 15° at 70 MHz). By contrast, the sparse array shows no observable grating lobes, confirming its superior suppression capability. At wide bandwidths, a sparse array optimized with a genetic algorithm outperforms a regular array in peak sidelobe level, sensitivity, and scanning range, supporting its use as a better technical solution for radio astronomical observations. 展开更多
关键词 HF-VHF band Sparse array Genetic algorithm Peak side lobe level Sensitivity
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Sparse optimization of planar radio antenna arrays using a genetic algorithm 认领 引用 被引量：1
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作者 Jiarui Di Liang Dong Wei He 《Astronomical Techniques and Instruments》 CSCD 2025年第2期100-110,共11页
Radio antenna arrays have many advantages for astronomical observations,such as high resolution,high sensitivity,multi-target simultaneous observation,and flexible beam formation.Problems surrounding key indices,such ... Radio antenna arrays have many advantages for astronomical observations,such as high resolution,high sensitivity,multi-target simultaneous observation,and flexible beam formation.Problems surrounding key indices,such as sensitivity enhancement,scanning range extension,and sidelobe level suppression,need to be solved urgently.Here,we propose a sparse optimization scheme based on a genetic algorithm for a 64-array element planar radio antenna array.As optimization targets for the iterative process of the genetic algorithm,we use the maximum sidelobe levels and beamwidth of multiple cross-section patterns that pass through the main beam in three-dimensions,with the maximum sidelobe levels of the patterns at several different scanning angles.Element positions are adjusted for iterations,to select the optimal array configuration.Following sparse layout optimization,the simulated 64-element planar radio antenna array shows that the maximum sidelobe level decreases by 1.79 dB,and the beamwidth narrows by 3°.Within the scan range of±30°,after sparse array optimization,all sidelobe levels decrease,and all beamwidths narrow.This performance improvement can potentially enhance the sensitivity and spatial resolution of radio telescope systems. 展开更多
关键词 Planar antenna array Sparse optimization Genetic algorithm Wide-angle scanning
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Ship Path Planning Based on Sparse A*Algorithm 认领 引用
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作者 Yongjian Zhai Jianhui Cui +3 位作者 Fanbin Meng Huawei Xie Chunyan Hou Bin Li 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第1期238-248,共11页
An improved version of the sparse A*algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorith... An improved version of the sparse A*algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorithms.This algorithm considers factors such as initial position and orientation of the ship,safety range,and ship draft to determine the optimal obstacle-avoiding route from the current to the destination point for ship planning.A coordinate transformation algorithm is also applied to convert commonly used latitude and longitude coordinates of ship travel paths to easily utilized and analyzed Cartesian coordinates.The algorithm incorporates a hierarchical chart processing algorithm to handle multilayered chart data.Furthermore,the algorithm considers the impact of ship length on grid size and density when implementing chart gridification,adjusting the grid size and density accordingly based on ship length.Simulation results show that compared to traditional path planning algorithms,the sparse A*algorithm reduces the average number of path points by 25%,decreases the average maximum storage node number by 17%,and raises the average path turning angle by approximately 10°,effectively improving the safety of ship planning paths. 展开更多
关键词 Sparse A*algorithm Path planning Rasterization Coordinate transformation Image preprocessing
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Sparse Recovery of Decaying Signals by the Piecewise Generalized Orthogonal Matching Pursuit Algorithm 认领 引用
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作者 Hanbing LIU Chongjun LI 《Journal of Mathematical Research with Applications》 CSCD 2025年第6期813-834,共22页
In this paper,we focus on the recovery of piecewise sparse signals containing both fast-decaying and slow-decaying nonzero entries.In order to improve the performance of classic Orthogonal Matching Pursuit(OMP)and Gen... In this paper,we focus on the recovery of piecewise sparse signals containing both fast-decaying and slow-decaying nonzero entries.In order to improve the performance of classic Orthogonal Matching Pursuit(OMP)and Generalized Orthogonal Matching Pursuit(GOMP)algorithms for solving this problem,we propose the Piecewise Generalized Orthogonal Matching Pursuit(PGOMP)algorithm,by considering the mixed-decaying sparse signals as piecewise sparse signals with two components containing nonzero entries with different decay factors.The algorithm incorporates piecewise selection and deletion to retain the most significant entries according to the sparsity of each component.We provide a theoretical analysis based on the mutual coherence of the measurement matrix and the decay factors of the nonzero entries,establishing a sufficient condition for the PGOMP algorithm to select at least two correct indices in each iteration.Numerical simulations and an image decomposition experiment demonstrate that the proposed algorithm significantly improves the support recovery probability by effectively matching piecewise sparsity with decay factors. 展开更多
关键词 piecewise sparse recovery decaying sparse signals mutual coherence greedy algorithm
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Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm 认领 引用 被引量：4
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作者 Jingjing Yu Jingxing Cheng +1 位作者 Yuqing Hou Xiaowei He 《Journal of Innovative Optical Health Sciences》 SCIE EI 2014年第3期50-58,共9页
Fluorescence molecular tomography(FMT)is a fast-developing optical imaging modalitythat has great potential in early diagnosis of disease and drugs development.However,recon-struction algorithms have to address a high... Fluorescence molecular tomography(FMT)is a fast-developing optical imaging modalitythat has great potential in early diagnosis of disease and drugs development.However,recon-struction algorithms have to address a highly ill-posed problem to fulfll 3D reconstruction inFMT.In this contribution,we propose an efficient iterative algorithm to solve the large-scalereconstruction problem,in which the sparsity of fluorescent targets is taken as useful a prioriinformation in designing the reconstruction algorithm.In the implementation,a fast sparseapproximation scheme combined with a stage-wise learning strategy enable the algorithm to dealwith the ill-posed inverse problem at reduced computational costs.We validate the proposed fastiterative method with numerical simulation on a digital mouse model.Experimental results demonstrate that our method is robust for different finite element meshes and different Poissonnoise levels. 展开更多
关键词 Fluorescence molecular tomography sparse regularization reconstruction algorithm least absolute shrinkage and selection operator.
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Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems 认领 引用 被引量：2
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作者 Sheng Qi Rui Wang +3 位作者 Tao Zhang Weixiong Huang Fan Yu Ling Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第8期1786-1801,共16页
Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to tr... Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges. 展开更多
关键词 Evolutionary algorithms pattern mining sparse large-scale multi-objective problems(SLMOPs) sparse large-scale optimization.
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Jointly-check iterative decoding algorithm for quantum sparse graph codes 认领 引用 被引量：2
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作者 邵军虎 白宝明 +1 位作者 林伟 周林 《Chinese Physics B》 SCIE EI CAS 2010年第8期116-122,共7页
For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with standard belief-propagation （BP） algorithm. ... For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with standard belief-propagation （BP） algorithm. In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently. Numerical simulations show that this modified method outperforms standard BP algorithm with an obvious performance improvement. 展开更多
关键词 quantum error correction sparse graph code iterative decoding belief-propagation algorithm
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A Local Sparse Screening Identification Algorithm with Applications 认领 引用 被引量：1
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作者 Hao Li Zhixia Wang Wei Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第8期765-782,共18页
Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors.Despite researchers follow the sparse identification nonlinear dynamics algorithm(SIND... Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors.Despite researchers follow the sparse identification nonlinear dynamics algorithm(SINDy)rule to restore nonlinear equations,there also exist obstacles.One is the excessive dependence on empirical parameters,which increases the difficulty of data pre-processing.Another one is the coexistence of multiple coefficient vectors,which causes the optimal solution to be drowned in multiple solutions.The third one is the composition of basic function,which is exclusively applicable to specific equations.In this article,a local sparse screening identification algorithm(LSSI)is proposed to identify nonlinear systems.First,we present the k-neighbor parameter to replace all empirical parameters in data filtering.Second,we combine the mean error screening method with the SINDy algorithm to select the optimal one from multiple solutions.Third,the time variable t is introduced to expand the scope of the SINDy algorithm.Finally,the LSSI algorithm is applied to recover a classic ODE and a bi-stable energy harvester system.The results show that the new algorithm improves the ability of noise immunity and optimal parameters identification provides a desired foundation for nonlinear analyses. 展开更多
关键词 The k-neighbor parameter sparse identification nonlinear dynamics algorithm mean error screening method the basic function energy harvester
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Federated Multi-Label Feature Selection via Dual-Layer Hybrid Breeding Cooperative Particle Swarm Optimization with Manifold and Sparsity Regularization 认领 引用
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作者 Songsong Zhang Huazhong Jin +5 位作者 Zhiwei Ye Jia Yang Jixin Zhang Dongfang Wu Xiao Zheng Dingfeng Song 《Computers, Materials & Continua》 SCIE EI 2026年第1期1141-1159,共19页
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal... Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics. 展开更多
关键词 Multi-label feature selection federated learning manifold regularization sparse constraints hybrid breeding optimization algorithm particle swarm optimizatio algorithm privacy protection
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Chaotic signal denoising algorithm based on sparse decomposition 认领 引用
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作者 Jin-Wang Huang Shan-Xiang Lv +1 位作者 Zu-Sheng Zhang Hua-Qiang Yuan 《Chinese Physics B》 SCIE EI CAS CSCD 2020年第6期133-138,共6页
Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics.The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristic... Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics.The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics.We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition(K-SVD)optimization.The observed signal is divided into segments and decomposed sparsely.The over-complete atomic library is constructed according to the differential equation of chaotic signals.The orthogonal matching pursuit algorithm is used to search the optimal matching atom.The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD.The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics. 展开更多
关键词 sparse decomposition denoising K-SVD chaotic signal
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Sensitivity Analysis of Structural Dynamic Behavior Based on the Sparse Polynomial Chaos Expansion and Material Point Method 认领 引用 被引量：2
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作者 Wenpeng Li Zhenghe Liu +4 位作者 Yujing Ma Zhuxuan Meng Ji Ma Weisong Liu Vinh Phu Nguyen 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第2期1515-1543,共29页
This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-... This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-oped using the material point method.To reduce the computational cost of Monte Carlo simulations,response surface models are created as surrogate models for the material point system to approximate its dynamic behavior.An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order,effectively balancing the accuracy and computational efficiency of the surrogate model.Based on the sparse polynomial chaos expansion,sensitivity analysis is conducted using the global finite difference and Sobol methods.Several examples of structural dynamics are provided to demonstrate the effectiveness of the proposed method in addressing structural dynamics problems. 展开更多
关键词 Structural dynamics deformation material point method sparse polynomial chaos expansion adaptive randomized greedy algorithm sensitivity analysis
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An Improved Proportionate Normalized Least Mean Square Algorithm for Sparse Impulse Response Identification 认领 引用
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作者 文昊翔 赖晓翰 +1 位作者 陈隆道 蔡忠法 《Journal of Shanghai Jiaotong university(Science)》 EI 2013年第6期742-748,共7页
In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coef... In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coefcient. Inspired by this result, a modified PNLMS algorithm based on precise magnitude estimate is proposed. The simulation results indicate that in contrast to the traditional PNLMS algorithm, the proposed algorithm achieves faster convergence speed in the initial convergence state and lower misalignment in the stead stage with much less computational complexity. 展开更多
关键词 adaptive algorithm echo cancellation(EC) proportionate normalized least mean square(PNLMS) algorithm proportionate step-size sparse impulse response
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New regularization method and iteratively reweighted algorithm for sparse vector recovery 认领 引用 被引量：2
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作者 Wei ZHU Hui ZHANG Lizhi CHENG 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2020年第1期157-172,共16页
Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design... Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm. 展开更多
关键词 regularization method iteratively reweighted algorithm(IR-algorithm) sparse vector recovery
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A SPARSE MATRIX TECHNIQUE FOR SIMULATING SEMICONDUCTOR DEVICES AND ITS ALGORITHMS 认领 引用 被引量：2
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作者 任建民 张义门 《Journal of Electronics(China)》 1990年第1期77-82,共6页
A novel sparse matrix technique for the numerical analysis of semiconductor devicesand its algorithms are presented.Storage scheme and calculation procedure of the sparse matrixare described in detail.The sparse matri... A novel sparse matrix technique for the numerical analysis of semiconductor devicesand its algorithms are presented.Storage scheme and calculation procedure of the sparse matrixare described in detail.The sparse matrix technique in the device simulation can decrease storagegreatly with less CPU time and its implementation is very easy.Some algorithms and calculationexamples to show the time and space characteristics of the sparse matrix are given. 展开更多
关键词 Semiconductor devices Sparse matrix technique Algorithm CAD
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Signal and Image Recovery with Scale and Signed Permutation Invariant Sparsity-Promoting Functions 认领 引用
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作者 Jianqing Jia Ashley Prater-Bennette Lixin Shen 《Analysis in Theory and Applications》 CSCD 2026年第1期62-89,共28页
Sparse signal recovery has been a cornerstone of advancements in data processing and imaging.Recently,the squared ratio of e1to e2norms,(e1/e2)2,has been introduced as a sparsity-prompting function,show... Sparse signal recovery has been a cornerstone of advancements in data processing and imaging.Recently,the squared ratio of e1to e2norms,(e1/e2)2,has been introduced as a sparsity-prompting function,showing superior performance compared to traditional`1 minimization,particularly in challenging scenarios with high coherence and dynamic range.This paper explores the integration of the proximity operator of(e1/e2)2and e1/e2into efficient optimization frameworks,including the Accelerated Proximal Gradient(APG)and Alternating Direction Method of Multipliers(ADMM).We rigorously analyze the convergence properties of these algorithms and demonstrate their effectiveness in compressed sensing and image restoration applications.Numerical experiments highlight the advantages of our proposed methods in terms of recovery accuracy and computational efficiency,particularly under noise and high-coherence conditions. 展开更多
关键词 Sparse signal recovery compressed sensing image restoration (e1/e2)2 optimization algorithms
A Robust Damage Identification Method Based on Modified Holistic Swarm Optimization Algorithm and Hybrid Objective Function 认领 引用
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作者 Xiansong Xie Xiaoqian Qian 《Structural Durability & Health Monitoring》 EI 2026年第2期235-259,共25页
Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this ... Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this issue,a robust structural damage identification method is developed,integrating a modified holistic swarm optimization(MHSO)algorithm with a hybrid objective function.The MHSO is developed by combining Hammersley sequencebased population initialization,chaotic search around the worst solution,and Hooke-Jeeves pattern search around the best solution,thereby improving both global exploration and local exploitation capabilities.A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions,effectively leveraging the complementary sensitivities of global and local responses.To further suppress spurious solutions and promote sparsity in parameter estimation,an additional L0.5 regularization term is introduced.The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure.Comparative studies with sequential quadratic programming,genetic algorithm,andHSO demonstrate that theMHSOachieves superior accuracy and convergence efficiency,even with limited sensors and 20%noise-contaminated measurements.Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages,while the inclusion of sparse regularization improves robustness against noise and model uncertainties.The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages,offering promising applicability to real-world structural health monitoring scenarios. 展开更多
关键词 Damage identification holistic swarm optimization algorithm combined correlation function hybrid objective function sparse regularization grid structure
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AN ACCELERATED PRECONDITIONED PRIMAL-DUAL GRADIENT ALGORITHM FOR NONCONVEX COMPOSITE OPTIMIZATION PROBLEMS WITH APPLICATIONS 认领 引用
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作者 Xian-Jun Long Jia-Lin Nie +1 位作者 Gao-Xi Li Zai-Yun Peng 《Journal of Computational Mathematics》 SCIE CSCD 2026年第6期1867-1889,共23页
In this paper,we consider a class of three-composite nonconvex optimization problems,in which the nonsmooth function is further composed with a linear operator.This problem has many applications such as sparse signal ... In this paper,we consider a class of three-composite nonconvex optimization problems,in which the nonsmooth function is further composed with a linear operator.This problem has many applications such as sparse signal recovery,image processing and machine learning.Based on the conjugate duality theory,we present an accelerated preconditioned primal-dual gradient algorithm for this problem.Compared with the existing algorithms,our algorithm only needs to calculate the proximal mapping of the conjugate function h*which is always convex and lower semicontinuous and it does not need to calculate the proximal mapping of nonconvex functions.This may significantly reduce the computation load.We prove that the sequence generated by the proposed algorithm globally converges to a critical point when the function satisfies the Kurdyka-Lojasiewicz property.We also obtain the convergence rate of the proposed algorithm.Finally,numerical results on sparse signal recovery and image processing illustrate the efficiency and competitiveness of the proposed algorithm. 展开更多
关键词 Nonconvex Composite Optimization Primal-dual Algorithm Convergence Sparse Signal Recovery Image Processing
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A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems 认领 引用
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作者 Yanxin Fu Wenxiao Zhao 《Control Theory and Technology》 EI CSCD 2024年第2期213-221,共9页
We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (... We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (SG) algorithm is applied to obtain initial estimates of the unknown parameter matrix and in the second step an optimization criterion is introduced for the sparse identification of multivariate ARX systems. Under mild conditions, we prove that by minimizing the criterion function, the zero elements of the unknown parameter matrix can be recovered with a finite number of observations. The performance of the algorithm is testified through a simulation example. 展开更多
关键词 ARX system Stochastic gradient algorithm Sparse identification Support recovery Parameter estimation Strong consistency
Sparse Planar Retrodirective Antenna Array Using Improved Adaptive Genetic Algorithm 认领 引用 被引量：3
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作者 Feng-Ge Hu Jian-Hua Zhang Li-Ye Fang 《Journal of Electronic Science and Technology》 CAS 2011年第3期265-269,共5页
An improved adaptive genetic algorithm is presented in this paper. It primarily includes two modified methods： one is novel adaptive probabilities of crossover and mutation, the other is truncated selection approach.... An improved adaptive genetic algorithm is presented in this paper. It primarily includes two modified methods： one is novel adaptive probabilities of crossover and mutation, the other is truncated selection approach. This algorithm has been validated to be superior to the simple genetic algorithm （SGA） by a complicated binary testing function. Then the proposed algorithm is applied to optimizing the planar retrodirective array to reduce the cost of the hardware. The fitness function is discussed in the optimization example. After optimization, the sparse planar retrodirective antenna array keeps excellent retrodirectivity, while the array architecture has been simplified by 34%. The optimized antenna array can replace uniform full array effectively. Results show that this work will gain more engineering benefits in practice. 展开更多
关键词 Index Terms Adaptive genetic algorithm phase conjugation retrodirective antenna array sparse array.
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基于K-SVD的时频谱特征稀疏编码的小样本未知辐射源盲聚类算法 认领 引用
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作者 李硕 陈之涵 +2 位作者 邹镇阳 张峻宁 严莹 《信息对抗技术》 CSCD 2026年第4期40-51,共12页
针对低信噪比、小样本条件下未知辐射源信号识别中的标签数据缺乏、泛化能力差的问题,本文提出一种基于K均值奇异值分解(K-means singular value decomposition,K-SVD)时频谱稀疏编码的盲聚类算法。首先,采用自适应奇异值分解(singular ... 针对低信噪比、小样本条件下未知辐射源信号识别中的标签数据缺乏、泛化能力差的问题,本文提出一种基于K均值奇异值分解(K-means singular value decomposition,K-SVD)时频谱稀疏编码的盲聚类算法。首先,采用自适应奇异值分解(singular value decomposition,SVD)滤波结合同步压缩小波变换,提升低信噪比信号的时频分辨率和清晰度。其次,构建K-SVD过完备差异字典,通过稀疏编码将时频谱映射至高维向量空间,提取信号调制样式的独特区分特征。最后,结合t分布随机近邻嵌入(t-distributed stochastic neighbor embedding,t-SNE)降维与带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,增强特征差异并实现高精度分类。相关实验结果表明,仅需小样本进行构建字典,在-14~4 dB信噪比下,算法对9类调制信号能达到96.41%的聚类精度;且利用仿真信号字典对外加实测异类信号聚类仍达到了94.25%的聚类精度,验证了算法的强泛化能力。 展开更多
关键词 辐射源识别 特征稀疏编码 字典向量空间 K-SVD算法 DBSCAN算法 盲聚类
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