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1.
This paper focuses on the joint parameter and state estimation issue for observer canonical state-space systems with white noises in state equations and moving average noises in output equations. By means of the Kalman filtering and the gradient search, we derive a Kalman filtering based extended stochastic gradient algorithm. For purpose of achieving the higher parameter estimation accuracy, a Kalman filtering based multi-innovation extended stochastic gradient algorithm is proposed on the basis of the multi-innovation identification theory. Finally, the effectiveness of the proposed algorithms is validated through a numerical example.  相似文献   

2.
This paper studies the parameter estimation problem of Hammerstein output error autoregressive (OEAR) systems. According to the maximum likelihood principle and the Levenberg–Marquardt optimization method, a maximum likelihood Levenberg–Marquardt recursive (ML-LM-R) algorithm using the varying interval input–output data is proposed. Furthermore, a stochastic gradient algorithm is also derived in order to compare it with the proposed ML-LM-R algorithm. Two numerical examples are provided to verify the effectiveness of the proposed algorithms.  相似文献   

3.
Two auxiliary model based recursive identification algorithms, a generalized extended stochastic gradient algorithm and a recursive generalized extended least squares algorithm, are developed for multivariable Box–Jenkins systems. The basic idea is to use the auxiliary models to estimate the unknown noise-free outputs of the system and to replace the unmeasurable terms in the information vectors with their estimates. We prove that the estimation errors given by the proposed algorithms converge to zero under the persistent excitation condition. Finally, an example is provided to show the effectiveness of the proposed algorithms.  相似文献   

4.
This paper surveys the identification of observer canonical state space systems affected by colored noise. By means of the filtering technique, a filtering based recursive generalized extended least squares algorithm is proposed for enhancing the parameter identification accuracy. To ease the computational burden, the filtered regressive model is separated into two fictitious sub-models, and then a filtering based two-stage recursive generalized extended least squares algorithm is developed on the basis of the hierarchical identification. The stochastic martingale theory is applied to analyze the convergence of the proposed algorithms. An experimental example is provided to validate the proposed algorithms.  相似文献   

5.
This paper considers the identification problem of bilinear systems with measurement noise in the form of the moving average model. In particular, we present an interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle. For unknown states, we formulate a novel bilinear state observer from input-output measurements using the Kalman filter. Then a bilinear state observer based multi-innovation extended stochastic gradient (BSO-MI-ESG) algorithm is proposed to estimate the unknown system parameters. A linear filter is utilized to improve the parameter estimation accuracy and a filtering based BSO-MI-ESG algorithm is presented using the data filtering technique. In the numerical example, we illustrate the effectiveness of the proposed identification methods.  相似文献   

6.
This paper focuses on the parameter estimation for radial basis function-based state-dependent autoregressive models with moving average noises (RBF-ARMA models). An extended projection algorithm is derived based on the negative gradient search. In order to reduce the sensitivity of the algorithm to noise and reduce the fluctuations of the parameter estimation errors, a modified extended stochastic gradient algorithm is proposed. By introducing a moving data window, a modified moving data window-based extended stochastic gradient algorithm is further developed to improve the parameter estimation accuracy. The simulation results show that the proposed algorithms can effectively estimate the parameters of the RBF-ARMA models.  相似文献   

7.
《Journal of The Franklin Institute》2022,359(17):10145-10171
Considering the colored noises from the process environments, the parameter estimation problems for the feedback nonlinear equation-error systems interfered by moving average noises are addressed in this paper. Due to small computational burden, the gradient search principle is adopted to the feedback nonlinear systems and an overall extended stochastic gradient algorithm is derived for parameter estimation. Introducing the innovation length, the scalar innovation is expanded into the innovation vector and a multi-innovation extended stochastic gradient algorithm is further developed to reach the high estimation accuracy by utilizing more dynamical observed data. Furthermore, to assure the convergence of the proposed algorithms, their convergence properties are analyzed through the stochastic process theory. Finally, the experimental results indicate the effectiveness of the proposed algorithms.  相似文献   

8.
This paper focuses on the parameter estimation problems of multivariate equation-error systems. A recursive generalized extended least squares algorithm is presented as a comparison. Based on the maximum likelihood principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification models, each of which has only a parameter vector, and a coupled subsystem maximum likelihood recursive least squares identification algorithm is developed for estimating the parameter vectors of these submodels. The simulation example shows that the proposed algorithm is effective and has high estimation accuracy.  相似文献   

9.
This paper focuses on the parameter estimation problem of multivariate output-error autoregressive systems. Based on the decomposition technique and the auxiliary model identification idea, we derive a decomposition based auxiliary model recursive generalized least squares algorithm. The key is to divide the system into two fictitious subsystems, the one including a parameter vector and the other including a parameter matrix, and to estimate the two subsystems using the recursive least squares method, respectively. Compared with the auxiliary model based recursive generalized least squares algorithm, the proposed algorithm has less computational burden. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithms.  相似文献   

10.
[目的/意义]旨在深入研究情境信息对用户偏好的影响,提高情境感知推荐的准确性。[方法/过程]提出了基于梯度提升决策树的情境感知推荐模型,根据梯度提升决策树计算情境属性权重,将其与传统协同过滤算法相融合,生成情境感知推荐结果。[结果/结论]该模型可以识别影响用户偏好的重要情景属性,为用户提供个性化推荐服务。  相似文献   

11.
This paper discusses the parameter estimation for a class of bilinear-in-parameter systems with colored noise. By utilizing the filtering technique, we derive the relationship between the filtered output and the measurement output and obtain two linear regressive sub-models. A filtering based multi-innovation stochastic gradient algorithm is derived for interactively identifying each sub-model. The proposed algorithm avoids the estimation of correlated noise and improves the parameter estimation accuracy by making full use of the measurement data. The numerical simulation results indicate that the proposed algorithm has higher estimation accuracy than the hierarchical multi-innovation stochastic gradient algorithm.  相似文献   

12.
Mathematical models are basic for designing controller and system identification is the theory and methods for establishing the mathematical models of practical systems. This paper considers the parameter identification for Hammerstein controlled autoregressive systems. Using the key term separation technique to express the system output as a linear combination of the system parameters, the system is decomposed into several subsystems with fewer variables, and then a hierarchical least squares (HLS) algorithm is developed for estimating all parameters involving in the subsystems. The HLS algorithm requires less computation than the recursive least squares algorithm. The computational efficiency comparison and simulation results both confirm the effectiveness of the proposed algorithms.  相似文献   

13.
This paper studies the parameter estimation problems of multivariate equation-error autoregressive moving average systems. Firstly, a gradient-based iterative algorithm is presented as a comparison. In order to improve the computational efficiency and the parameter estimation accuracy, a decomposition-based gradient iterative algorithm is presented by using the decomposition technique. The key is to transform an original system into two subsystems and to estimate the parameters of each subsystem, respectively. Compared with the gradient-based iterative algorithm, the decomposition-based algorithm requires less computational efforts, and the simulation results indicate that this algorithm is effective.  相似文献   

14.
The piecewise-linear characteristics often appear in the nonlinear systems that operate in different ways in different input regions. This paper studies the identification issue of a class of block-oriented systems with piecewise-linear characteristics. The asymmetric piecewise-linear nonlinearity is expressed as a linear parametric representation through introducing an appropriate switching function, then the identification model of the system is derived by using the key term separation technique. On this model basis, a multi-innovation forgetting gradient algorithm is presented to estimate the unknown parameters. To further enhance the identification accuracy, the filtering identification model of the system is derived by changing the structure of the system without changing the relationship between the input and output. Further, a data filtering-based multi-innovation forgetting gradient algorithm is proposed through the use of the data filtering technique. A simulation example is employed to illustrate that the proposed approaches are effective for parameter estimation and the data filtering-based multi-innovation forgetting gradient algorithm has better estimation performance.  相似文献   

15.
This article proposes an affine-projection-like maximum correntropy (APLMC) algorithm for robust adaptive filtering. The proposed APLMC algorithm is derived by using the objective function based on the maximum correntropy criterion (MCC), which can availably suppress the bad effects of impulsive noise on filter weight updates. But the overall performance of the APLMC algorithm may be decreased when the input signal is polluted by noise. To compensate for the deviation of the APLMC algorithm in the input noise interference environment, the bias compensation (BC) method is introduced. Therefore, the bias-compensated APLMC (BC-APLMC) algorithm is presented. Besides, the convergence of the BC-APLMC algorithm in the mean and the mean square sense is studied, which provides a constraint range for the step-size. Computer simulation results show that the APLMC, and BC-APLMC algorithms are valid in acoustic echo cancellation and system identification applications. It also shows that the proposed algorithms are robust in the presence of input noise and impulse noise.  相似文献   

16.
The performance of the residual-based extended stochastic gradient (ESG) algorithms for identifying CARMA models with disturbances is analyzed under weaker conditions on statistical properties of the noise. The paper derives the conditions under which the parameter estimation errors converge to zero. Three examples are given to show the advantages of the proposed algorithm.  相似文献   

17.
基于改进遗传算法的高光谱图像波段选择   总被引：3，自引：0，他引：3  
在对地观测领域,高光谱图像得到了广泛应用,但存在数据量大、波段间相关性高等问题. 针对以上问题分析了已有的波段选择方法,提出了基于信息量及类间可分离性准则的遗传算法对高光谱图像进行波段选择:构造波段互相关系数矩阵进行子空间划分;利用联合熵作为组合信息量的标准,Bhattacharyya距离作为类间可分离性标准,构造遗传算法的适应度方程,改进了遗传算法中的选择算子. 最后用AVIRIS图像对提出的算法进行试验,并利用最大似然分类法对最优波段组合进行分类,总体分类精度达到94.24%,Kappa系数达到0.94.  相似文献   

18.
For multichannel signal filtering or detection in unknown noise, it is usually difficult to obtain sufficient independent and identically distributed (IID) training data in real-world applications, which considerably degrades the performance of adaptive algorithms. In this paper, we consider the problem of subspace signal filtering and detection in sample-starved environment. A simple reduced-dimension approach is adopted, which alleviates the requirement of IID training data. First, the test and training data are projected onto the signal subspace. Then we adopt the criterion of the generalized likelihood ratio test (GLRT) to devise a detector, which can also serve as a filter. The resulting detector can properly work in sample-starved environment, where the number of IID training data is less than the dimension of the test data. Moreover, the devised approach is superior to the existing adaptive subspace processor in filtering and detection, even in some sample-abundant situations. Analytical expressions for the probabilities of detection and false alarm are derived for the proposed approach. Numerical examples are given to verify its effectiveness.  相似文献   

19.
Maximum likelihood methods are significant for parameter estimation and system modeling. This paper gives the input-output representation of a bilinear system through eliminating the state variables in it, and derives a maximum likelihood least squares based iterative for identifying the parameters of bilinear systems with colored noises by using the maximum likelihood principle. A least squares based iterative (LSI) algorithm is presented for comparison. It is proved that the maximum of the likelihood function is equivalent to minimize the least squares cost function. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems and the maximum likelihood LSI algorithm is more accurate than the LSI algorithm.  相似文献   

20.
The conventional per-survivor-processing (PSP) scheme suffers from the error propagation problem because it does not fully use the state message provided by a hidden Markov process. This study proposes a vertical cooperation among states to enhance the estimate reliabilities in the PSP scheme. The key idea of the proposed algorithm is to estimate the system uncertainty at each time stage in a maximum likelihood (ML) manner that chooses the parameter estimate of the state with the minimum cumulative branch metric as the survival estimate of the time stage. Computer simulations show that with the improved phase estimates, the proposed algorithm significantly outperforms the conventional PSP scheme in data decoding.  相似文献   

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