In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the ...In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the GNSS spoofing is proposed.First,a Hammerstein model is applied to model the spoofer/GNSS transmitter and the wireless channel.Then,a novel method based on the uncultivated wolf pack algorithm（UWPA） is proposed to estimate the model parameters.Taking the estimated model parameters as a feature vector,the identification of the spoofing is realized by comparing the Euclidean distance between the feature vectors.Simulations verify the effectiveness and the robustness of the proposed method.The results show that,compared with the other identification algorithms,such as least square（LS）,the iterative method and the bat-inspired algorithm（BA）,although the UWPA has a little more time-eomplexity than the LS and the BA algorithm,it has better estimation precision of the model parameters and higher identification rate of the GNSS spoofing,even for relative low signal-to-noise ratios.展开更多
Two new methods, the generalized Levy method and the weighted iteration method, are presented for identification of non-integer order systems. The first method generalizes the Levy identification method from the integ...Two new methods, the generalized Levy method and the weighted iteration method, are presented for identification of non-integer order systems. The first method generalizes the Levy identification method from the integer order systems to the non-integer order systems. Then, the weighted iteration method is presented to overcome the shortcomings of the first method. Results show that the proposed methods have better performance compared with the integer order identification method. For the non-integer order systems, the proposed methods have the better fitting for the system frequency response. For the integer order system, if commensurate order scanning is applied, the proposed methods can also achieve the best integer order model which fits the system frequency response. At the same time, the proposed algorithms are more stable.展开更多
A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is appl...A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method.展开更多
The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimatio...The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimation accuracy of soft sensor models. Wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from Wiener model structure, which is an extension of Wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradient algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model, alternate identification algorithm, and update method.展开更多
In the last two decades, the damage detection for civil engineering structures has been widely treated as a modal analysis problem and most of the currently available vibration-based system identification approaches a...In the last two decades, the damage detection for civil engineering structures has been widely treated as a modal analysis problem and most of the currently available vibration-based system identification approaches are based on modal parameters, namely the natural frequencies, mode shapes and damping ratios, and/or their derivations, which are suitable for linear systems. Nonlinearity is generic in engineering structures. For example, the initiation and development of cracks in civil engineering structures as typical structural damages are nonlinear process. One of the major challenges in damage detection, early warning and damage prognosis is to obtain reasonably accurate identification of nonlinear performance such as hysteresis which is the direct indicator of damage initiation and development under dynamic excitations. In this study, a general data-based identification approach for hysteretic performance in form of nonlinear restoring force using structural dynamic responses and complete and incomplete excitation measurement time series was proposed and validated with a 4-story frame structure equipped with smart devices of magneto-theological （MR） damper to simulate nonlinear performance. Firstly, as an optimization method, the least-squares technique was employed to identify the system matrices of an equivalent linear system of the nonlinear structure model basing on the exci- tation force and the corresponding vibration measurements with impact test when complete and incomplete excitations; and secondly, the nonlinear restoring force of the structure was identified and compared with the test measurements fi- nally. Results show that the proposed data-based approach is capable of identifying the nonlinear behavior of engineering structures and can be employed to evaluate the damage initiation and development of different structure under dynamic loads.展开更多
Using the inversion of the auto correlation function Toeplitz matrix of pseudo random binary sequence (PRBS) derived in this paper and the theorem of partitioned matrix inversion, a fast multistage least squares (FM...Using the inversion of the auto correlation function Toeplitz matrix of pseudo random binary sequence (PRBS) derived in this paper and the theorem of partitioned matrix inversion, a fast multistage least squares (FMLS) method is developed. Its performances are theoretically analyzed and digital simulation is made to compare FMLS with multistage least squares (MSLS), correlation least squares(COR LS) and LS for their computer speed and identification accuracy. Finally, FMLS is applied to identifying the heat excharger dynamics. It is shown that FMLS is a good and effective identification technique.展开更多
The paper concerns a research into dynamic properties of the steel suspension bridge across Opolska Street in Krakow, Poland. Parameter identification was carried out with the application of the nonlinear system ident...The paper concerns a research into dynamic properties of the steel suspension bridge across Opolska Street in Krakow, Poland. Parameter identification was carried out with the application of the nonlinear system identification method on the basis of system responses to exploitational excitation resulting from pedestrian traffic. In order to verify obtained results, on the basis of the geometrical and material properties of the considered system, the FEM （finite elements model） was created. Created FEM model was updated through the comparison with the model determined by the use of experimental modal analysis method and then applied to analytical evaluation of the considered suspension bridge natural frequencies.展开更多
A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to el...A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.展开更多
The identification of Wiener systems has been an active research topic for years. A Wiener system is a series connection of a linear dynamic system followed by a static nonlinearity. The difficulty in obtaining a repr...The identification of Wiener systems has been an active research topic for years. A Wiener system is a series connection of a linear dynamic system followed by a static nonlinearity. The difficulty in obtaining a representation of the Wiener model is the need to estimate the nonlinear function from the input and output data, without the intermediate signal availability. This paper presents a methodology for the nonlinear system identification of a Wiener type model, using methods for subspaces and polynomials of Chebyshev. The subspace methods used are MOESP （multivariable output-error state space） and N4SID （numerical algorithms for subspace state space system identification）. A simulated example is presented to compare the performance of these algorithms.展开更多
基金The National Natural Science Foundation of China(No.61271214,61471152)the Postdoctoral Science Foundation of Jiangsu Province(No.1402023C)the Natural Science Foundation of Zhejiang Province(No.LZ14F010003)
文摘In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the GNSS spoofing is proposed.First,a Hammerstein model is applied to model the spoofer/GNSS transmitter and the wireless channel.Then,a novel method based on the uncultivated wolf pack algorithm（UWPA） is proposed to estimate the model parameters.Taking the estimated model parameters as a feature vector,the identification of the spoofing is realized by comparing the Euclidean distance between the feature vectors.Simulations verify the effectiveness and the robustness of the proposed method.The results show that,compared with the other identification algorithms,such as least square（LS）,the iterative method and the bat-inspired algorithm（BA）,although the UWPA has a little more time-eomplexity than the LS and the BA algorithm,it has better estimation precision of the model parameters and higher identification rate of the GNSS spoofing,even for relative low signal-to-noise ratios.
文摘Two new methods, the generalized Levy method and the weighted iteration method, are presented for identification of non-integer order systems. The first method generalizes the Levy identification method from the integer order systems to the non-integer order systems. Then, the weighted iteration method is presented to overcome the shortcomings of the first method. Results show that the proposed methods have better performance compared with the integer order identification method. For the non-integer order systems, the proposed methods have the better fitting for the system frequency response. For the integer order system, if commensurate order scanning is applied, the proposed methods can also achieve the best integer order model which fits the system frequency response. At the same time, the proposed algorithms are more stable.
文摘A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method.
基金Supported by the National Natural Science Foundation of China(61104218,21006127)the National Basic Research Program of China(2012CB720500)the Science Foundation of China University of Petroleum(YJRC-2013-12)
文摘The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimation accuracy of soft sensor models. Wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from Wiener model structure, which is an extension of Wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradient algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model, alternate identification algorithm, and update method.
基金The authors gratefully acknowledge the support provided through the National Natural Science Foundation of China (NSFC) under grant No. 50608031the Hunan Provincial Natural Science Foundation of China under grant No.08JJ1009the Key Project of Chinese Ministry of Education (No. 108102)
文摘In the last two decades, the damage detection for civil engineering structures has been widely treated as a modal analysis problem and most of the currently available vibration-based system identification approaches are based on modal parameters, namely the natural frequencies, mode shapes and damping ratios, and/or their derivations, which are suitable for linear systems. Nonlinearity is generic in engineering structures. For example, the initiation and development of cracks in civil engineering structures as typical structural damages are nonlinear process. One of the major challenges in damage detection, early warning and damage prognosis is to obtain reasonably accurate identification of nonlinear performance such as hysteresis which is the direct indicator of damage initiation and development under dynamic excitations. In this study, a general data-based identification approach for hysteretic performance in form of nonlinear restoring force using structural dynamic responses and complete and incomplete excitation measurement time series was proposed and validated with a 4-story frame structure equipped with smart devices of magneto-theological （MR） damper to simulate nonlinear performance. Firstly, as an optimization method, the least-squares technique was employed to identify the system matrices of an equivalent linear system of the nonlinear structure model basing on the exci- tation force and the corresponding vibration measurements with impact test when complete and incomplete excitations; and secondly, the nonlinear restoring force of the structure was identified and compared with the test measurements fi- nally. Results show that the proposed data-based approach is capable of identifying the nonlinear behavior of engineering structures and can be employed to evaluate the damage initiation and development of different structure under dynamic loads.
文摘Using the inversion of the auto correlation function Toeplitz matrix of pseudo random binary sequence (PRBS) derived in this paper and the theorem of partitioned matrix inversion, a fast multistage least squares (FMLS) method is developed. Its performances are theoretically analyzed and digital simulation is made to compare FMLS with multistage least squares (MSLS), correlation least squares(COR LS) and LS for their computer speed and identification accuracy. Finally, FMLS is applied to identifying the heat excharger dynamics. It is shown that FMLS is a good and effective identification technique.
文摘The paper concerns a research into dynamic properties of the steel suspension bridge across Opolska Street in Krakow, Poland. Parameter identification was carried out with the application of the nonlinear system identification method on the basis of system responses to exploitational excitation resulting from pedestrian traffic. In order to verify obtained results, on the basis of the geometrical and material properties of the considered system, the FEM （finite elements model） was created. Created FEM model was updated through the comparison with the model determined by the use of experimental modal analysis method and then applied to analytical evaluation of the considered suspension bridge natural frequencies.
基金Supported in part by Chinese Recruitment Program of Global Young Expert,Alexander von Humboldt Research Fellowship of Germany,the Foundamental Research Funds for the Central Universitiesthe National Natural Science Foundation of China (61074020)
文摘A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.
文摘The identification of Wiener systems has been an active research topic for years. A Wiener system is a series connection of a linear dynamic system followed by a static nonlinearity. The difficulty in obtaining a representation of the Wiener model is the need to estimate the nonlinear function from the input and output data, without the intermediate signal availability. This paper presents a methodology for the nonlinear system identification of a Wiener type model, using methods for subspaces and polynomials of Chebyshev. The subspace methods used are MOESP （multivariable output-error state space） and N4SID （numerical algorithms for subspace state space system identification）. A simulated example is presented to compare the performance of these algorithms.
