The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the m...The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.展开更多
Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin s...Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).展开更多
Anti-aliasing spectrum analysis is essential for rotor blade condition monitoring based on Blade Tip Timing(BTT).The Multiple Signal Classification(MUSIC)algorithm,which exploits the orthogonality between signal and n...Anti-aliasing spectrum analysis is essential for rotor blade condition monitoring based on Blade Tip Timing(BTT).The Multiple Signal Classification(MUSIC)algorithm,which exploits the orthogonality between signal and noise subspaces,has been successfully applied for this purpose.However,conventional subspace selection methods relying on fixed thresholds are sensitive to variations in large eigenvalues.Furthermore,the complex disturbances during rotor operation and measurement complicate the identification of blade vibration characteristics.To overcome these challenges,this paper proposes Adaptive Subspace Separation(ASS)and Local Spectral Centroid(LSC)methods to improve the adaptability of subspace selection and the stability of frequency identification,respectively.The impacts of overestimating and underestimating the subspace dimensions on MUSIC's performance are derived mathematically.Simulation and experiments demonstrate the effectiveness of proposed approaches:ASS offers more accurate and stable subspace dimension selection and tracking,while LSC reduces the standard deviation of estimated frequencies by 30 percent.展开更多
Adiabatic holonomic gates possess the geometric robustness of adiabatic geometric phases,i.e.,dependence only on the evolution path of the parameter space but not on the evolution details of the quantum system,which,w...Adiabatic holonomic gates possess the geometric robustness of adiabatic geometric phases,i.e.,dependence only on the evolution path of the parameter space but not on the evolution details of the quantum system,which,when coordinated with decoherence-free subspaces,permits additional resilience to the collective dephasing environment.However,the previous scheme[Phys.Rev.Lett.95130501(2005)]of adiabatic holonomic quantum computation in decoherence-free subspaces requires four-body interaction that is challenging in practical implementation.In this work,we put forward a scheme to realize universal adiabatic holonomic quantum computation in decoherence-free subspaces using only realistically available two-body interaction,thereby avoiding the difficulty of implementing four-body interaction.Furthermore,an arbitrary one-qubit gate in our scheme can be realized by a single-shot implementation,which eliminates the need to combine multiple gates for realizing such a gate.展开更多
The problem of pattern-based subspace clustering, a special type of subspace clustering that uses pattern similarity as a measure of similarity, is studied. Unlike most traditional clustering algorithms that group the...The problem of pattern-based subspace clustering, a special type of subspace clustering that uses pattern similarity as a measure of similarity, is studied. Unlike most traditional clustering algorithms that group the close values of objects in all the dimensions or a set of dimensions, clustering by pattern similarity shows an interesting pattern, where objects exhibit a coherent pattern of rise and fall in subspaces. A novel approach, named EMaPle to mine the maximal pattern-based subspace clusters, is designed. The EMaPle searches clusters only in the attribute enumeration spaces which are relatively few compared to the large number of row combinations in the typical datasets, and it exploits novel pruning techniques. EMaPle can find the clusters satisfying coherent constraints, size constraints and sign constraints neglected in MaPle. Both synthetic data sets and real data sets are used to evaluate EMaPle and demonstrate that it is more effective and scalable than MaPle.展开更多
In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because t...In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because the transmission equation of OFDM systems does not exactly have the desired structure to directly derive a subspace algorithm,the algorithm first divides the OFDM signals into three parts,then,by exploiting the redundancy introduced by the cyclic prefix (CP) in OFDM signals,a new equation with Toeplitz channel matrix is derived.Based on the equation,a new blind subspace algorithm is developed.Toeplitz structure eases the derivation of the subspace algorithm and practical computation.Moreover the algorithm does not change the existing OFDM system,is robust to channel order overdetermination,and the channel zero locations.The performances are demonstrated by simulation results.展开更多
The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model fo...The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.展开更多
A 2D-direction of arrival estimation (DOAE) for multi input and multi-output (MIMO) radar using improved multiple temporal-spatial subspaces in estimating signal parameters via rotational invariance techniques method ...A 2D-direction of arrival estimation (DOAE) for multi input and multi-output (MIMO) radar using improved multiple temporal-spatial subspaces in estimating signal parameters via rotational invariance techniques method (TS-ESPRIT) is introduced. In order to realize the improved TS-ESPRIT, the proposed algorithm divides the planar array into multiple uniform sub-planar arrays with common reference point to get a unified phase shifts measurement point for all sub-arrays. The TS-ESPRIT is applied to each sub-array separately, and in the same time with the others to realize the parallelly temporal and spatial processing, so that it reduces the non-linearity effect of model and decreases the computational time. Then, the time difference of arrival (TDOA) technique is applied to combine the multiple sub-arrays in order to form the improved TS-ESPRIT. It is found that the proposed method achieves high accuracy at a low signal to noise ratio (SNR) with low computational complexity, leading to enhancement of the estimators performance.展开更多
In this paper, third-order nonlinear differential operators are studied. It is shown that they are quadratic forms when they preserve invariant subspaces of maximal dimension. A complete description of third-order qua...In this paper, third-order nonlinear differential operators are studied. It is shown that they are quadratic forms when they preserve invariant subspaces of maximal dimension. A complete description of third-order quadratic operators with constant coefficients is obtained. One example is given to derive special solutions for evolution equations with third-order quadratic operators.展开更多
Stochastic Subspace Identification (SSI) is a novel time domain identification method, which directly uses operational response data to identify the system model by linear algebraic manipulations such as QR facto...Stochastic Subspace Identification (SSI) is a novel time domain identification method, which directly uses operational response data to identify the system model by linear algebraic manipulations such as QR factorization and Singular Value Decomposition (SVD). This paper deals with SSI and its applications for structural modal identification. The NASA mini mast model is used for simulations to illustrate how to select input parameters, and to demonstrate identification precision. A real building structure, the Heritage Court Tower (HCT) in Canada is analyzed. From the simulation and test researches, the conclusions can be made to instruct how to identify structural modal parameters using SSI method.展开更多
The invariant subspace method is used to construct the explicit solution of a nonlinear evolution equation. The second-order nonlinear differential operators that possess invariant subspaces of submaximal dimension ar...The invariant subspace method is used to construct the explicit solution of a nonlinear evolution equation. The second-order nonlinear differential operators that possess invariant subspaces of submaximal dimension are described. There are second-order nonlinear differential operators, including cubic operators and quadratic operators, which preserve an invariant subspace of submaximal dimension. A full. description, of the second-order cubic operators with constant coefficients admitting a four-dimensional invariant subspace is given. It is shown that the maximal dimension of invaxiant subspaces preserved by a second-order cubic operator is four. Several examples are given for the construction of the exact solutions to nonlinear evolution equations with cubic nonlinearities. These solutions blow up in a finite展开更多
In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model ...In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model which performs control.As a frst step,the main geometric and mathematical tools used in subspace identifcation are briefly presented.In the second step,the problem of analyzing ill-conditioning matrices in the subspace identifcation method is considered.To illustrate this situation,a simulation study of an example is introduced to show the ill-conditioning in subspace identifcation.Algorithms numerical subspace state space system identifcation(N4SID)and multivariable output error state space model identifcation(MOESP)are considered to study,the parameters estimation while using the induction motor model,in simulation(Matlab environment).Finally,we show the inadequacy of the oblique projection and validate the efectiveness of the orthogonal projection approach which is needed in ill-conditioning;a real application dealing with induction motor parameters estimation has been experimented.The obtained results proved that the algorithm based on orthogonal projection MOESP,overcomes the situation of ill-conditioning in the Hankel s block,and thereby improving the estimation of parameters.展开更多
The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities...The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity,leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals,and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this method,three data types are used,and seven common similarity measurement methods are compared.The experimental result indicates that the relative difference of the method is increasing with the dimensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition,the similarity range of this method in different dimensions is [0,1],which is fit for similarity analysis after dimensionality reduction.展开更多
The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive pro...The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest （SOI） component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom （DOF） of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.展开更多
In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombina...In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained.展开更多
Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predict...Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predictive control algorithm(SPC). The new method uses model matching error to calculate the variable forgetting factor, and applies it to constructing Hankel data matrix.This makes the data represent the changes of system information better. For eliminating the steady state error, the derivation of the incremental control is made. Simulation results on a rotary kiln show that this control strategy has achieved a good control effect.展开更多
In this paper, the on-orbit identification of modal parameters for a spacecraft is investigated. Firstly, the coupled dynamic equation of the system is established with the Lagrange method and the stochastic state-spa...In this paper, the on-orbit identification of modal parameters for a spacecraft is investigated. Firstly, the coupled dynamic equation of the system is established with the Lagrange method and the stochastic state-space model of the system is obtained. Then, the covariance-driven stochastic subspace identification（SSI-COV） algorithm is adopted to identify the modal parameters of the system. In this algorithm, it just needs the covariance of output data of the system under ambient excitation to construct a Toeplitz matrix, thus the system matrices are obtained by the singular value decomposition on the Toeplitz matrix and the modal parameters of the system can be found from the system matrices. Finally,numerical simulations are carried out to demonstrate the validity of the SSI-COV algorithm. Simulation results indicate that the SSI-COV algorithm is effective in identifying the modal parameters of the spacecraft only using the output data of the system under ambient excitation.展开更多
It will be determined under what conditions types of proximinality are transmitted to and from quotient spaces. In the final section, by many examples we show that types of proximinality of subspaces in Banach spaces ...It will be determined under what conditions types of proximinality are transmitted to and from quotient spaces. In the final section, by many examples we show that types of proximinality of subspaces in Banach spaces can not be preserved by equivalent norms.展开更多
In this paper, a bias-eliminated subspace identification method is proposed for industrial applications subject to colored noise. Based on double orthogonal projections, an identification algorithm is developed to eli...In this paper, a bias-eliminated subspace identification method is proposed for industrial applications subject to colored noise. Based on double orthogonal projections, an identification algorithm is developed to eliminate the influence of colored noise for consistent estimation of the extended observability matrix of the plant state-space model. A shift-invariant approach is then given to retrieve the system matrices from the estimated extended observability matrix. The persistent excitation condition for consistent estimation of the extended observability matrix is analyzed. Moreover, a numerical algorithm is given to compute the estimation error of the estimated extended observability matrix. Two illustrative examples are given to demonstrate the effectiveness and merit of the proposed method.展开更多
基金Project(2010ZX03006-004) supported by the National Science and Technology Major Program of ChinaProject(YYYJ-1113) supported by the Knowledge Innovation Program of the Chinese Academy of SciencesProject(2011CB302901) supported by the National Basic Research Program of China
文摘The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.
基金supported by the National Key R&D Program of China(2023YFC3304600).
文摘Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).
基金supported by the National Natural Science Foundation of China(Nos.52405088 and 92360306)the Postdoctoral Fellowship Program of CPSF,China(No.GZC20241446)+2 种基金the Natural Science Basic Research Program of Shaanxi,China(No.2024JC-YBMS-402)the Fundamental Research Funds for the Central Universities,CHD(No.300102254102)the Foundation of Beilin District,China(No.GX2455)。
文摘Anti-aliasing spectrum analysis is essential for rotor blade condition monitoring based on Blade Tip Timing(BTT).The Multiple Signal Classification(MUSIC)algorithm,which exploits the orthogonality between signal and noise subspaces,has been successfully applied for this purpose.However,conventional subspace selection methods relying on fixed thresholds are sensitive to variations in large eigenvalues.Furthermore,the complex disturbances during rotor operation and measurement complicate the identification of blade vibration characteristics.To overcome these challenges,this paper proposes Adaptive Subspace Separation(ASS)and Local Spectral Centroid(LSC)methods to improve the adaptability of subspace selection and the stability of frequency identification,respectively.The impacts of overestimating and underestimating the subspace dimensions on MUSIC's performance are derived mathematically.Simulation and experiments demonstrate the effectiveness of proposed approaches:ASS offers more accurate and stable subspace dimension selection and tracking,while LSC reduces the standard deviation of estimated frequencies by 30 percent.
基金Project supported by the National Natural Science Foundation of China(Grant No.12305021)。
文摘Adiabatic holonomic gates possess the geometric robustness of adiabatic geometric phases,i.e.,dependence only on the evolution path of the parameter space but not on the evolution details of the quantum system,which,when coordinated with decoherence-free subspaces,permits additional resilience to the collective dephasing environment.However,the previous scheme[Phys.Rev.Lett.95130501(2005)]of adiabatic holonomic quantum computation in decoherence-free subspaces requires four-body interaction that is challenging in practical implementation.In this work,we put forward a scheme to realize universal adiabatic holonomic quantum computation in decoherence-free subspaces using only realistically available two-body interaction,thereby avoiding the difficulty of implementing four-body interaction.Furthermore,an arbitrary one-qubit gate in our scheme can be realized by a single-shot implementation,which eliminates the need to combine multiple gates for realizing such a gate.
基金The National Natural Science Foundation of China(No60273075)
文摘The problem of pattern-based subspace clustering, a special type of subspace clustering that uses pattern similarity as a measure of similarity, is studied. Unlike most traditional clustering algorithms that group the close values of objects in all the dimensions or a set of dimensions, clustering by pattern similarity shows an interesting pattern, where objects exhibit a coherent pattern of rise and fall in subspaces. A novel approach, named EMaPle to mine the maximal pattern-based subspace clusters, is designed. The EMaPle searches clusters only in the attribute enumeration spaces which are relatively few compared to the large number of row combinations in the typical datasets, and it exploits novel pruning techniques. EMaPle can find the clusters satisfying coherent constraints, size constraints and sign constraints neglected in MaPle. Both synthetic data sets and real data sets are used to evaluate EMaPle and demonstrate that it is more effective and scalable than MaPle.
文摘In order to increase the transmission efficiency,a subspace-based algorithm for blind channel estimation using second-order statistics is proposed in orthogonal frequency division multiplexing (OFDM) systems.Because the transmission equation of OFDM systems does not exactly have the desired structure to directly derive a subspace algorithm,the algorithm first divides the OFDM signals into three parts,then,by exploiting the redundancy introduced by the cyclic prefix (CP) in OFDM signals,a new equation with Toeplitz channel matrix is derived.Based on the equation,a new blind subspace algorithm is developed.Toeplitz structure eases the derivation of the subspace algorithm and practical computation.Moreover the algorithm does not change the existing OFDM system,is robust to channel order overdetermination,and the channel zero locations.The performances are demonstrated by simulation results.
基金funded by the National Natural Science Foundation of China (Nos. 11572069 and 51775541)the China Postdoctoral Science Foundation (No. 2016M601354)
文摘The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.
基金supported by the National Natural Science Foundation of China(61301211)and the Aviation Science Foundation(20131852028)
文摘A 2D-direction of arrival estimation (DOAE) for multi input and multi-output (MIMO) radar using improved multiple temporal-spatial subspaces in estimating signal parameters via rotational invariance techniques method (TS-ESPRIT) is introduced. In order to realize the improved TS-ESPRIT, the proposed algorithm divides the planar array into multiple uniform sub-planar arrays with common reference point to get a unified phase shifts measurement point for all sub-arrays. The TS-ESPRIT is applied to each sub-array separately, and in the same time with the others to realize the parallelly temporal and spatial processing, so that it reduces the non-linearity effect of model and decreases the computational time. Then, the time difference of arrival (TDOA) technique is applied to combine the multiple sub-arrays in order to form the improved TS-ESPRIT. It is found that the proposed method achieves high accuracy at a low signal to noise ratio (SNR) with low computational complexity, leading to enhancement of the estimators performance.
基金supported by the National Natural Science Foundation of China(Grant No.11371293)the Civil Military Integration Research Foundation of Shaanxi Province,China(Grant No.13JMR13)+2 种基金the Natural Science Foundation of Shaanxi Province,China(Grant No.14JK1246)the Mathematical Discipline Foundation of Shaanxi Province,China(Grant No.14SXZD015)the Basic Research Project Foundation of Weinan City,China(Grant No.2013JCYJ-4)
文摘In this paper, third-order nonlinear differential operators are studied. It is shown that they are quadratic forms when they preserve invariant subspaces of maximal dimension. A complete description of third-order quadratic operators with constant coefficients is obtained. One example is given to derive special solutions for evolution equations with third-order quadratic operators.
文摘Stochastic Subspace Identification (SSI) is a novel time domain identification method, which directly uses operational response data to identify the system model by linear algebraic manipulations such as QR factorization and Singular Value Decomposition (SVD). This paper deals with SSI and its applications for structural modal identification. The NASA mini mast model is used for simulations to illustrate how to select input parameters, and to demonstrate identification precision. A real building structure, the Heritage Court Tower (HCT) in Canada is analyzed. From the simulation and test researches, the conclusions can be made to instruct how to identify structural modal parameters using SSI method.
基金Project supported by the National Natural Science Foundation of China(Grant No.10926082)the Natural Science Foundation of Anhui Province of China(Grant No.KJ2010A128)the Fund for Youth of Anhui Normal University,China(Grant No.2009xqn55)
文摘The invariant subspace method is used to construct the explicit solution of a nonlinear evolution equation. The second-order nonlinear differential operators that possess invariant subspaces of submaximal dimension are described. There are second-order nonlinear differential operators, including cubic operators and quadratic operators, which preserve an invariant subspace of submaximal dimension. A full. description, of the second-order cubic operators with constant coefficients admitting a four-dimensional invariant subspace is given. It is shown that the maximal dimension of invaxiant subspaces preserved by a second-order cubic operator is four. Several examples are given for the construction of the exact solutions to nonlinear evolution equations with cubic nonlinearities. These solutions blow up in a finite
基金supported by the Ministry of Higher Education and Scientific Research of Tunisia
文摘In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model which performs control.As a frst step,the main geometric and mathematical tools used in subspace identifcation are briefly presented.In the second step,the problem of analyzing ill-conditioning matrices in the subspace identifcation method is considered.To illustrate this situation,a simulation study of an example is introduced to show the ill-conditioning in subspace identifcation.Algorithms numerical subspace state space system identifcation(N4SID)and multivariable output error state space model identifcation(MOESP)are considered to study,the parameters estimation while using the induction motor model,in simulation(Matlab environment).Finally,we show the inadequacy of the oblique projection and validate the efectiveness of the orthogonal projection approach which is needed in ill-conditioning;a real application dealing with induction motor parameters estimation has been experimented.The obtained results proved that the algorithm based on orthogonal projection MOESP,overcomes the situation of ill-conditioning in the Hankel s block,and thereby improving the estimation of parameters.
基金Supported by the National Natural Science Foundation of China(No.61502475)the Importation and Development of High-Caliber Talents Project of the Beijing Municipal Institutions(No.CIT&TCD201504039)
文摘The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity,leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals,and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this method,three data types are used,and seven common similarity measurement methods are compared.The experimental result indicates that the relative difference of the method is increasing with the dimensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition,the similarity range of this method in different dimensions is [0,1],which is fit for similarity analysis after dimensionality reduction.
文摘The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest （SOI） component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom （DOF） of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.
基金Supported by the National Natural Science Foundation of China (70071042,60073043,60133010)
文摘In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained.
文摘Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predictive control algorithm(SPC). The new method uses model matching error to calculate the variable forgetting factor, and applies it to constructing Hankel data matrix.This makes the data represent the changes of system information better. For eliminating the steady state error, the derivation of the incremental control is made. Simulation results on a rotary kiln show that this control strategy has achieved a good control effect.
基金supported by the National Natural Science Foundation of China(Grants 11132001,11272202,11472171)the Key Scientific Project of Shanghai Municipal Education Commission(Grant 14ZZ021)+1 种基金the Natural Science Foundation of Shanghai(Grant 14ZR1421000)the Special Fund for Talent Development of Minhang District of Shanghai
文摘In this paper, the on-orbit identification of modal parameters for a spacecraft is investigated. Firstly, the coupled dynamic equation of the system is established with the Lagrange method and the stochastic state-space model of the system is obtained. Then, the covariance-driven stochastic subspace identification（SSI-COV） algorithm is adopted to identify the modal parameters of the system. In this algorithm, it just needs the covariance of output data of the system under ambient excitation to construct a Toeplitz matrix, thus the system matrices are obtained by the singular value decomposition on the Toeplitz matrix and the modal parameters of the system can be found from the system matrices. Finally,numerical simulations are carried out to demonstrate the validity of the SSI-COV algorithm. Simulation results indicate that the SSI-COV algorithm is effective in identifying the modal parameters of the spacecraft only using the output data of the system under ambient excitation.
文摘It will be determined under what conditions types of proximinality are transmitted to and from quotient spaces. In the final section, by many examples we show that types of proximinality of subspaces in Banach spaces can not be preserved by equivalent norms.
基金This work was supported by the National Thousand Talents Program of China, the National Natural Science Foundation of China （Nos. 61473054, 61633006）, and the Fundamental Research Funds for the Central Universities of China （No. DUT15ZD108）.
文摘In this paper, a bias-eliminated subspace identification method is proposed for industrial applications subject to colored noise. Based on double orthogonal projections, an identification algorithm is developed to eliminate the influence of colored noise for consistent estimation of the extended observability matrix of the plant state-space model. A shift-invariant approach is then given to retrieve the system matrices from the estimated extended observability matrix. The persistent excitation condition for consistent estimation of the extended observability matrix is analyzed. Moreover, a numerical algorithm is given to compute the estimation error of the estimated extended observability matrix. Two illustrative examples are given to demonstrate the effectiveness and merit of the proposed method.
