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1.
This paper focuses on parameter estimation problems for non-uniformly sampled Hammerstein nonlinear systems. By combining the lifting technique and state space transformation, we derive a nonlinear regression identification model with different input and output updating rates. Furthermore, the unmeasurable state vector is estimated by Kalman filter, and by using the hierarchical identification principle, we develop a hierarchical recursive least squares algorithm for estimating the unknown parameters of the identification model. Finally, illustrative examples are given to indicate that the proposed algorithm is effective.  相似文献   

2.
This paper puts forward a robust identification solution for nonlinear time-delay state-space model (NDSSM) with contaminated measurements. To enhance the robustness of the developed method for outliers, the heavy-tailed Laplace distribution is employed to describe and protect the output measurement process. The undetermined time-delay is considered to be uniformly distributed and the boundary of it is known as a priori. In the developed solution, the uncertain time-delay is treated as a latent process variable and it is iteratively calculated with the expectation–maximization (EM) algorithm. The EM algorithm is actually an iterative optimization algorithm and it is effective for the hidden variable problems. The particle filter is introduced to numerically approximate the cost function (Q-function) in the EM algorithm since it is difficult to calculate directly. The efficacy of the developed solution is evaluated via a numerical test and a two-link robotic manipulator.  相似文献   

3.
This paper is concerned with the modeling and prediction of random delays in networked control systems. Considering the Gaussian mixture distribution followed by the random delays in real networks, the semi-continuous hidden Markov model (SCHMM) is proposed in this paper to model the random delays. The initialization and optimization problems of the model parameters are solved by using the K-mean clustering algorithm and the expectation maximization algorithm. Based on the model, the prediction of the controller-to-actuator (CA) delay in the current sampling period is obtained. The prediction can be used to design a controller to compensate the CA delay in the future research. Two illustrative examples are given to demonstrate the effectiveness and superiority of the proposed modeling and prediction methods.  相似文献   

4.
In traditional system identification methods, it is often assumed that the output data are corrupted by Gaussian white noise which is independent and identically distributed (i.i.d.). However, this assumption may lead to poor robustness since the noise characteristic often varies throughout the sampling process. In this work, output measurements affected by switching Gaussian noise are considered. In addition, a Markov chain model is utilized to describe the multi-mode behavior of the noises. Meanwhile, the collected data are usually incomplete in practice. Taking these circumstances into account, a new algorithm for Gaussian process regression (GPR) with switching noise mode and missing data is introduced. The parameters of the model are estimated by expectation maximization (EM) algorithm via conjugate gradient (CG) method. Two numerical examples along with a continuous stirred tank reactor simulation are employed to verify the effectiveness of the proposed algorithm. The superior performance is demonstrated by comparing the proposed algorithm with other existing relevant methods.  相似文献   

5.
This paper presents a moving horizon estimation approach for the multirate sampled-data system with unknown time-delay sequence. To estimate the unknown variables of interest, two main challenging issues need to be addressed: (a) synthesizing the multirate input and output data for state estimation, (b) simultaneously estimating the continuous state and discrete time-delay sequence. In this work a moving horizon estimation based approach is developed to tackle these issues. The proposed approach can simultaneously estimate both the continuous states and discrete time-delay sequence for dynamic systems. The effects of different noise level on the estimation of continuous states and discrete time-delay sequence are analyzed. The effectiveness of this method is illustrated through a simulation study.  相似文献   

6.
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   

7.
This paper considers the parameter identification problem of a bilinear state space system with colored noise based on its input-output representation. An input-output representation of a bilinear state-space system is derived for the parameter identification by eliminating the state variables in the model, and a recursive generalized extended least squares algorithm is presented for estimating the parameters of the obtained model. Furthermore, a three-stage recursive generalized extended least squares algorithm is proposed for reducing the computational cost. The validity of the proposed method is evaluated through a numerical example.  相似文献   

8.
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   

9.
Identification of switched finite impulse response (FIR) systems in the presence of random missing outputs is investigated in this paper and the practical problems of unknown number of local models and unknown switching mechanism are handled. From a Bayesian perspective, the probabilistic model for describing the identification problem is constructed and the algorithm to estimate all of the unknown parameters is derived by using the variational Bayesian (VB) approach. In addition, the number of local models can be selected based on the probability of each local component, and the predicted output can be obtained as the output of the local model that takes effect. A simulated example and the mass-spring-damper system are explored to illustrate the efficacy of the developed algorithm.  相似文献   

10.
Previous studies have adopted unsupervised machine learning with dimension reduction functions for cyberattack detection, which are limited to performing robust anomaly detection with high-dimensional and sparse data. Most of them usually assume homogeneous parameters with a specific Gaussian distribution for each domain, ignoring the robust testing of data skewness. This paper proposes to use unsupervised ensemble autoencoders connected to the Gaussian mixture model (GMM) to adapt to multiple domains regardless of the skewness of each domain. In the hidden space of the ensemble autoencoder, the attention-based latent representation and reconstructed features of the minimum error are utilized. The expectation maximization (EM) algorithm is used to estimate the sample density in the GMM. When the estimated sample density exceeds the learning threshold obtained in the training phase, the sample is identified as an outlier related to an attack anomaly. Finally, the ensemble autoencoder and the GMM are jointly optimized, which transforms the optimization of objective function into a Lagrangian dual problem. Experiments conducted on three public data sets validate that the performance of the proposed model is significantly competitive with the selected anomaly detection baselines.  相似文献   

11.
This paper presents the sliding mode mean-square and mean-module state filtering and parameter identification problems for linear stochastic systems with unknown parameters over linear observations, where unknown parameters are considered Wiener processes. The original problems are reduced to the sliding mode mean-square and mean-module filtering problems for an extended state vector that incorporates parameters as additional states. The obtained sliding mode filters for the extended state vector also serve as the optimal identifiers for the unknown parameters. Performance of the designed sliding mode mean-square and mean-module state filters and parameter identifiers are verified for both, stable and unstable, linear uncertain systems.  相似文献   

12.
This paper focuses on the identification of multiple-input single-output output-error systems with unknown time-delays. Since the time-delays are unknown, an identification model with a high dimensional and sparse parameter vector is derived based on overparameterization. Traditional identification methods cannot get sparse solutions and require a large number of observations unless the time-delays are predetermined. Inspired by the sparse optimization and the greedy algorithms, an auxiliary model based orthogonal matching pursuit iterative (AM-OMPI) algorithm is proposed by using the orthogonal matching pursuit, and then based on the gradient search, an auxiliary model based gradient pursuit iterative algorithm is proposed, which is computationally more efficient than the AM-OMPI algorithm. The proposed methods can simultaneously estimate the parameters and time-delays from a small number of sampled data. A simulation example is used to illustrate the effectiveness of the proposed algorithms.  相似文献   

13.
This paper researches parameter estimation problems for an input nonlinear system with state time-delay. Combining the linear transformation and the property of the shift operator, the system is transformed into a bilinear parameter identification model. A gradient based and a least squares based iterative parameter estimation algorithms are presented for identifying the state time-delay system. The simulation results confirm that the proposed two algorithms are effective and the least squares based iterative algorithm has faster convergence rates than the gradient based iterative algorithm.  相似文献   

14.
詹士昌  徐婕  吴俊 《科技通报》2004,20(2):138-141
蚁群算法是一种模拟进化算法，初步的研究表明该算法具有许多优良的性质．研究了一种可用于求解连续空间优化问题的蚁群算法策略，针对SISO离散时不变控制系统，在给出了加权矩阵Q与状态反馈阵K的取值范围确定方法的基础上，应用连续性空间优化问题的蚁群算法模型求解了离散LQ逆问题。仿真结果表明蚁群算法在求解控制优化问题中的有效性。  相似文献   

15.
This paper investigates the application of deep reinforcement learning (RL) in the motion control for an autonomous underwater vehicle (AUV), and proposes a novel general motion control framework which separates training and deployment. Firstly, the state space, action space, and reward function are customized under the condition of ensuring generality for various motion control tasks. Next, in order to efficiently learn the optimal motion control policy in the case that the AUV model is imprecise and there are unknown external disturbances, a virtual AUV model composed of the known and determined items of an actual AUV is put forward and a simulation training method is developed on this basis. Then, in the given deployment method, three independent extended state observers (ESOs) are designed to deal with the unknown items in different directions, and the final controller is obtained by compensating the estimated value of ESOs into the output of the optimal motion control policy obtained through simulation training. Finally, soft actor-critic is chosen as deep RL algorithm of the framework, and the generality and effectiveness of the proposed method are verified in four different AUV motion control tasks.  相似文献   

16.
总结了多项目管理和冲突管理的文献资料,利用修正的聚类评价模型构建冲突测度指标的模块架构,勾勒包括个人心理预期状态、对抗性和组织文化认同等在内的10个维度和其包含的33个单项指标。  相似文献   

17.
在采用空时码的无线通信系统中,收发2端较差的路径会降低系统的性能.提出了多速率线性离散码(LDC),并对其进行了分析,给出了一种基于矩阵扩展方法的设计算法.多速率LDC是传统LDC和LDC-TAS的BER性能的折中.进而提出了一种性能优于传统LDC-A-TAS算法的多速率LDC自适应发送天线选择(A.TAS)算法.  相似文献   

18.
In this paper, the identification of the Wiener–Hammerstein systems with unknown orders linear subsystems and backlash is investigated by using the modified multi-innovation stochastic gradient identification algorithm. In this scheme, in order to facilitate subsequent parameter identification, the orders of linear subsystems are firstly determined by using the determinant ratio approach. To address the multi-innovation length problem in the conventional multi-innovation least squares algorithm, the innovation updating is decomposed into sub-innovations updating through the usage of multi-step updating technique. In the identification procedure, by reframing two auxiliary models, the unknown internal variables are replaced by using the outputs of the corresponding auxiliary model. Furthermore, the convergence analysis of the proposed algorithm has shown that the parameter estimation error can converge to zero. Simulation examples are provided to validate the efficiency of the proposed algorithm.  相似文献   

19.
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   

20.
This paper considers the parameter and order estimation for multiple-input single-output nonlinear systems. Since the orders of the system are unknown, a high-dimensional identification model and a sparse parameter vector are established to include all the valid inputs and basic parameters. Applying the data filtering technique, the input-output data are filtered and the original identification model with autoregressive noise is changed into the identification model with white noise. Based on the compressed sensing recovery theory, a data filtering-based orthogonal matching pursuit algorithm is presented for estimating the system parameters and the orders. The presented method can obtain highly accurate estimates from a small number of measurements by finding the highest absolute inner product. The simulation results confirm that the proposed algorithm is effective for recovering the model of the multiple-input single-output Hammerstein finite impulse response systems.  相似文献   

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