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| 1 | 改进的梯度下降法在离散时滞系统中的应用显示文摘提出了离散时滞系统的辨识问题,这个问题包括时滞参数的辨识和系统动态参数的辨识。提出了一种基于辅助模型思想和低通滤波技术的带可变遗忘因子的随机梯度下降法对系统时滞参数和系统动态参数同时进行在线辨识。建立辅助模型,用辅助模型输出代替系统的无噪声输出。引入基于预测误差的可变遗忘因子,加快算法的收敛速度,提高算法的预测精度。同时,引入低通滤波技术增大时滞参数的收敛域,使得损失函数取得全局最优。最后,通过仿真证明了本文算法的有效性。 | 杨晓冬 马光 | 2016 | 国外电子测量技术2016，35，3： | 2 |
| 2 | Optimal Systematic Determination of Models' Base for Multimodel Representation: Real Time Application显示文摘The multimodel approach is a powerful and practical tool to deal with analysis, modeling, observation, emulation and control of complex systems. In the modeling framework, we propose in this paper a new method for optimal systematic determination of models base for multimodel representation. This method is based on the classification of data set picked out of the considered system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models base. A simulation example and an experimental validation on a semi-batch reactor are presented to evaluate the effectiveness of the proposed method. | Majda Ltaief Anis Messaoud Ridha Ben Abdennour | 2014 | International Journal of Automation and computing2014，11，6： | 1 |
| 3 | 有色噪声干扰下时滞过程输出误差模型的迭代辨识(英文)显示文摘To deal with colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares(ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method. | 董世健 刘涛 李明忠 曹毅 | 2015 | Chinese Journal of Chemical Engineering2015，23，12： | 1 |
| 4 | 基于时效关联分析的氧化铝蒸发过程多重时滞辨识显示文摘将流程工业中物料在每个单元的滞留时间以整数序列的形式表示,根据时滞序列将原始数据重新匹配生成新的数据矩阵。定义时效关联分析来描述数据矩阵中各个时间序列之间的相关性并使用H∞范数进行量化,将多重时滞辨识问题转化为求取最大H∞范数的问题。最后采用离散状态转移算法进行快速求解,得到最优时基序列即为辨识得到的多重时滞。将所提方法用于氧化铝蒸发过程的多重时滞辨识,将辨识出的多重时滞用于预测模型数据的预处理,预测模型的准确性提高了34.4%,验证了辨识方法的有效性。 | 王峰 王晓丽 谢永芳 谢森 阳春华 | 2017 | 化工学报2017，68，3： | 1 |
| 5 | Discretization of Nonlinear Non-affine Time Delay Systems Based on Second-order Hold显示文摘When calculating the sampled-date representation of nonlinear systems second-order hold(SOH) assumption can be applied to improving the precision of the discretization results. This paper proposes a discretization method based on Taylor series and the SOH assumption for the nonlinear systems with the time delayed non-affine input. The mathematical structure of the proposed discretization method is explored. This proposed discretization method can provide a precise and finite dimensional discretization model for the nonlinear time-delayed non-affine system by keeping the truncation order of the Taylor series. The performance of the proposed discretization method is evaluated by doing the simulation using a nonlinear system with the time-delayed non-affine input.Different input signals, time-delay values and sampling periods are considered in the simulation to investigate the proposed method.The simulation results demonstrate that the proposed method is practical and easy for time-delayed nonlinear non-affine systems.The comparison between SOH assumption with first-order hold(FOH) and zero-order hold(ZOH) assumptions is given to show the advantages of the proposed method. | Yuan-Liang Zhang | 2014 | International Journal of Automation and computing2014，11，3： | 0 |
| 6 | 基于依赖性度量的Hammerstein系统的时滞估计显示文摘时滞在工业系统中广泛存在,但是现有的Hammerstein辨识算法大多没有考虑时滞,使得这些算法的应用受到局限。针对具有整数时滞的Hammerstein非线性系统,提出了一种新的时滞估计方法。该方法基于输入、输出数据的依赖性工作,首先计算输入数据和输出数据的边缘分布;其次基于2-copula函数构建它们的联合分布,利用该分布定义一个依赖性度量方法;最后基于这个度量推断时滞。该方法独立于参数估计时滞,并且避免了基于优化的时滞估计方法中存在的取整运算问题。通过数值实验和实际案例,验证了该方法可以准确估计Hammerstein系统的时滞。 | 安秋羽 景绍学 | 2024 | 软件工程2024，27，2： | 0 |
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