共查询到20条相似文献，搜索用时 31 毫秒
1.
In this paper, two relaxed gradient-based iterative algorithms for solving a class of generalized coupled Sylvester-conjugate matrix equations are proposed. The proposed algorithm is different from the gradient-based iterative algorithm and the modified gradient-based iterative algorithm that are recently available in the literature. With the real representation of a complex matrix as a tool, the sufficient and necessary condition for the convergence factor is determined to guarantee that the iterative solution given by the proposed algorithms converge to the exact solution for any initial matrices. Moreover, some sufficient convergence conditions for the suggested algorithms are presented. Finally, numerical example is provided to illustrate the effectiveness of the proposed algorithms and testify the conclusions suggested in this paper. 相似文献
2.
《Journal of The Franklin Institute》2014,351(12):5326-5339
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
3.
《Journal of The Franklin Institute》2023,360(11):7206-7229
This paper focuses on the numerical solution of a class of generalized coupled Sylvester-conjugate matrix equations, which are general and contain many significance matrix equations as special cases, such as coupled discrete-time/continuous-time Markovian jump Lyapunov matrix equations, stochastic Lyapunov matrix equation, etc. By introducing the modular operator, a cyclic gradient based iterative (CGI) algorithm is provided. Different from some previous iterative algorithms, the most significant improvement of the proposed algorithm is that less information is used during each iteration update, which is conducive to saving memory and improving efficiency. The convergence of the proposed algorithm is discussed, and it is verified that the algorithm converges for any initial matrices under certain assumptions. Finally, the effectiveness and superiority of the proposed algorithm are verified with some numerical examples. 相似文献
4.
In this paper, we discuss the properties of the eigenvalues related to the symmetric positive definite matrices. Several new results are established to express the structures and bounds of the eigenvalues. Using these results, a family of iterative algorithms are presented for the matrix equation AX=F and the coupled Sylvester matrix equations. The analysis shows that the iterative solutions given by the least squares based iterative algorithms converge to their true values for any initial conditions. The effectiveness of the proposed iterative algorithm is illustrated by a numerical example. 相似文献
5.
The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms. 相似文献
6.
This paper considers the parameter identification problems of the input nonlinear output-error (IN-OE) systems, that is the Hammerstein output-error systems. In order to overcome the excessive calculation amount of the over-parameterization method of the IN-OE systems. Through applying the hierarchial identification principle and decomposing the IN-OE system into three subsystems with a smaller number of parameters, we present the key term separation auxiliary model hierarchical gradient-based iterative algorithm and the key term separation auxiliary model hierarchical least squares-based iterative algorithm, which are called the key term separation auxiliary model three-stage gradient-based iterative algorithm and the key term separation auxiliary model three-stage least squares-based iterative algorithm. The comparison of the calculation amount and the simulation analysis indicate that the proposed algorithms are effective. 相似文献
7.
《Journal of The Franklin Institute》2022,359(9):4468-4488
The conjugate gradient (CG) method exhibits fast convergence speed than the steepest descent, which has received considerable attention. In this work, we propose two CG-based methods for nonlinear active noise control (NLANC). The proposed filtered-s Bessel CG (FsBCG)-I algorithm implements the functional link artificial neural network (FLANN) as a controller, and it is derived from the Matérn kernel to achieve enhanced performance in various environments. On the basis of the FsBCG-I algorithm, we further develop the FsBCG-II algorithm, which utilizes the Bessel function of the first kind to constrain outliers. As an alternative, the FsBCG-II algorithm has reduced computational complexity and similar performance as compared to the FsBCG-I algorithm. Moreover, the convergence property of the algorithms is analyzed. The proposed algorithms are compared with some highly cited previous works. Extensive simulation results demonstrate that the proposed algorithms can achieve robust performance when the noise source is impulsive, Gaussian, logistic, and time-varying. 相似文献
8.
《Journal of The Franklin Institute》2019,356(11):5819-5833
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
9.
In this work, a lifted event-triggered iterative learning control (lifted ETILC) is proposed aiming for addressing all the key issues of heterogeneous dynamics, switching topologies, limited resources, and model-dependence in the consensus of nonlinear multi-agent systems (MASs). First, we establish a linear data model for describing the I/O relationships of the heterogeneous nonlinear agents as a linear parametric form to make the non-affine structural MAS affine with respect to the control input. Both the heterogeneous dynamics and uncertainties of the agents are included in the parameters of the linear data model, which are then estimated through an iterative projection algorithm. On this basis, a lifted event-triggered learning consensus is proposed with an event-triggering condition derived through a Lyapunov function. In this work, no threshold condition but the event-triggering condition is used which plays a key role in guaranteeing both the stability and the iterative convergence of the proposed lifted ETILC. The proposed method can reduce the number of control actions significantly in batches while guaranteeing the iterative convergence of tracking error. Both rigorous analysis and simulations are provided and confirm the validity of the lifted ETILC. 相似文献
10.
《Journal of The Franklin Institute》2022,359(4):1407-1426
This paper presents a novel iterative learning feedback control method for linear parabolic distributed parameter systems with multiple collocated piecewise observation. Multiple actuators and sensors distributed at the same position of the spatial domain are utilized to perform collocated piecewise control and measurement operations. The advantage of the proposed method is that it combines the iterative learning algorithm and feedback technique. Not only can it use the iterative learning algorithm to track the desired output trajectory, but also the feedback control approach can be utilized to achieve real-time online update. By utilizing integration by parts, triangle inequality, mean value theorem for integrals and Gronwall lemma, two sufficient conditions based on the inequality constraints for the convergence analysis of the tracking error system are presented. Some simulation experiments are provided to prove the effectiveness of the proposed method. 相似文献
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在MB-OFDM超宽带系统中,针对《无线高速率超宽带物理层和媒体访问控制规范》国家标准中MAC层数据结构,提出了一种卡尔曼滤波改进的反馈信道估计算法。该算法是在卡尔曼滤波之前加入一个多级LS算法的预估价。与传统卡尔曼滤波比较,该方法提高了卡尔曼滤波的收敛速度,简化了卡尔曼滤波的迭代过程,最小均方误差性能和误码率性能也略有提升。 相似文献
13.
《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
14.
Lingling Lv Jinbo Chen Zhe Zhang Baowen Wang Lei Zhang 《Journal of The Franklin Institute》2021,358(3):2039-2059
This paper studies the numerical solutions of a class of periodic coupled matrix equations. Based on the least square method, a finite iterative algorithm for a class of periodic coupled matrix equations is proposed, and the convergence of the algorithm is proved by theoretical derivation. For any initial value, the algorithm can converge to the solution in finite iterations. Since the equations considered in paper contain many variants, the proposed algorithm has a wide range of applications. Finally some numerical examples in practical systems are given to prove the effectiveness and efficiency of the algorithm. 相似文献
15.
This paper deals with the problem of iterative learning control for a class of singular systems with one-sided Lipschitz nonlinearity. In order to track the given desired trajectory, a closed-loop D-type learning algorithm is proposed for such nonlinear singular systems. Then the convergence result is derived by utilizing the one-sided Lipschitz and quadratically inner-bounded conditions. In this work, the main contribution is to apply the iterative learning approach to one-sided Lipschitz singular systems, while most researches are focus on the Lipschitz systems. It is shown that the algorithm can guarantee the system output converges to the desired trajectory on the whole time interval. Finally, the effectiveness of the presented algorithm is verified by a numerical example. 相似文献
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17.
Mohammad Shams Esfand Abadi Sima Kadkhodazadeh 《Journal of The Franklin Institute》2011,348(2):212-238
In this paper, the concept of proportionate adaptation is extended to the normalized subband adaptive filter (NSAF), and seven proportionate normalized subband adaptive filter algorithms are established. The proposed algorithms are proportionate normalized subband adaptive filter (PNSAF), μ‐law PNSAF (MPNSAF), improved PNSAF (IPNSAF), the improved IPNSAF (IIPNSAF), the set-membership IPNSAF (SM-IPNSAF), the selective partial update IPNSAF (SPU-IPNSAF), and SM-SPU-IPNSAF which are suitable for sparse system identification in network echo cancellation. When the impulse response of the echo path is sparse, the PNSAF has initial faster convergence than NSAF but slows down dramatically after initial convergence. The MPNSAF algorithm has fast convergence speed during the whole adaptation. The IPNSAF algorithm is suitable for both sparse and dispersive impulse responses. The SM-IPNSAF exhibits good performance with significant reduction in the overall computational complexity compared with the ordinary IPNSAF. In SPU-IPNSAF, the filter coefficients are partially updated rather than the entire filter at every adaptation. In SM-SPU-IPNSAF algorithm, the concepts of SM and SPU are combined which leads to a reduction in computational complexity. The simulation results show good performance of the proposed algorithms. 相似文献
18.
In this paper, based on the Smith iteration (Smith, 1968), an inner-outer (IO) iteration algorithm for solving the coupled Lyapunov matrix equations (CLMEs) is presented. First, the IO iteration algorithm for solving the Sylvester matrix equation is proposed, and its convergence is analyzed in detail. Second, the IO iteration algorithm for solving the CLMEs is constructed. By utilizing the latest estimation, a current-estimation-based and two weighted IO iteration algorithms are also given for solving the CLMEs, respectively. Convergence analyses indicate that the iteration solutions generated by these algorithms always converge to the unique solutions to the CLMEs for any initial conditions. Finally, Several numerical examples are provided to show the superiority of the proposed numerical algorithms. 相似文献
19.
Xuehai Wang Feng Ding Ahmed Alsaedi Tasawar Hayat 《Journal of The Franklin Institute》2017,354(1):593-609
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
20.
《Journal of The Franklin Institute》2019,356(11):5801-5818
This paper focuses on the recursive parameter estimation methods for the exponential autoregressive (ExpAR) model. Applying the negative gradient search and introducing a forgetting factor, a stochastic gradient and a forgetting factor stochastic gradient algorithms are presented. In order to improve the parameter estimation accuracy and the convergence rate, the multi-innovation identification theory is employed to derive a forgetting factor multi-innovation stochastic gradient algorithm. A simulation example is provided to test the effectiveness of the proposed algorithms. 相似文献