共查询到18条相似文献,搜索用时 187 毫秒
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利用小波逼近的软阈方法,研究了离散非线性系统的WorstCase辨识问题。证明了该算法在Worst-Case误差下的拟最优性和光滑性;估计了该算法的Worst-Case误差;给出了存在鲁棒收敛的辨识算法的充要条件;最后,证明了小波网逼近算法是鲁棒收敛的。 相似文献
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讨论单输入单输出,离散时不变因果系统的L1系统辩识问题。首先提出基于代数方法的代数算法,并分析了该算法的特点;然后估计其Worst-case误差,并证明了该算法的收敛性;最后讨论了在某些特殊情况下该算法的相应形式。所给结果是面向鲁棒控制的。 相似文献
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现有的l^1鲁棒辨识方法依赖于观测数据窗的起始时刻因而不能用来辨识时变系统,针对该问题基于最小二乘法提出了一种l^1鲁棒辨识算法.该算法与观测窗的起始时刻无关,可用于时变系统的辨识.证明了当试验输入为持续激励信号时所提出的算法为本质最优算法,进一步证明了周期持续激励序列为最优试验信号,并给出了辨识误差紧界的计算公式.最后利用提出的算法研究了慢时变系统的l^1鲁棒辨识问题. 相似文献
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研究了一类基于动态神经网络的未知非线性多变量系统的鲁棒辨识问题,用Lyapunov稳定性理论获得了具有保护策略的鲁棒调权律,从理论上证明了被辨识的系统是鲁榛 ,辨识误差按建模误差和未建模动态收敛到一个稳定区域,该策略的特点是不需要离线学习又不需要对象的状态完全可测,仿真结果验证了提出的动态网鲁棒辨识策略的有效性。 相似文献
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基于小波变换估计频域模型误差界 总被引:2,自引:0,他引:2
讨论鲁棒辨识问题,基于离散小波变换,提出分段频带逼近,估计频域模型界。首先介绍离散小波变换,然后给出分段频带逼近算法,仿真结果验证所提方法。 相似文献
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对一类非线性离散时间系统提出一种新的模糊的辨识方法。该方法在假设逼近误差界已知的情况下,基于死区函数对模糊逻辑系统中的未知参数设计自适应学习律;在逼近误差界未知的情况下,基于时变死区函数对模糊逻辑系统中的未知参数设计自适应学习律,并对时变死区进行自适应调节。证明了所设计的自适应学习律均可使辨识误差收敛到原点的一个小邻域内。仿真结果表明了该算法的有效性。 相似文献
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系统地讨论了SISO、线性时不变、指数稳定系统在最坏情况下的l^1鲁棒辨识问题。提出了系统模型集合的最小外框概念,建立了两种任意非零信号作用下l^1鲁棒辨识算法;提出了任意非零信号作用下系统的可辨识条件;证明了算法的全局收敛性和最优性。 相似文献
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A linear algorithm and a nonlinear algorithm for the problem of system identification in H ∞ posed by Helmicki et al. (1990) for discrete-time systems are presented. The authors derive some error bounds for the linear algorithm which indicate that it is not robustly convergent. However, the worst-case identification error is shown to grow as log(n ), where n is the model order. A robustly convergent nonlinear algorithm is derived, and bounds on the worst-case identification error (in the H ∞ norm) are obtained 相似文献
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In this paper, the problem of ‘system identification in ??∞’ is investigated in the case when the given frequency response data are not necessarily on a uniformly spaced grid of frequencies. A large class of robustly convergent identification algorithms is derived. A particular algorithm is further examined and explicit worst case error bounds (in the ??∞ norm) are derived for both discrete-time and continuous-time systems. An example is provided to illustrate the application of the algorithms. 相似文献
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基于辅助模型的量化控制系统辨识方法 总被引:1,自引:1,他引:0
针对具有通信约束的量化控制系统模型, 在采用随机重复性试验测量信息的技术上, 提出了基于辅助模型的量化系统参数辨识方法. 首先分析了在随机重复性试验方法下量化系统的模型特征并给出了分两步辨识的策略.分析表明, 在上述模型里系统具有时变的估计误差, 推导了进行参数辨识所满足的持续激励条件, 并给出了基于辅助模型的多新息量化辨识递推算法. 接着研究了所给出辨识算法的收敛性分析, 得到了系统参数估计误差上界的计算式,最后将方法推广到一类Hammerstein非线性系统量化辨识问题上. 数字仿真验证了该算法及结论 相似文献
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Er-Wei BaiAuthor Vitae 《Automatica》2002,38(5):853-860
This paper studies identification of systems with input nonlinearities of known structure. For input nonlinearities parameterized by one parameter, a deterministic approach is proposed based on the idea of separable least squares. The identification problem is shown to be equivalent to an one-dimensional minimization problem. The method is very effective for several common static and nonstatic input nonlinearities. For a general input nonlinearity, a correlation analysis based identification algorithm is presented which is shown to be convergent. 相似文献
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In this paper, we study the identification of parametric Hammerstein systems with FIR linear parts. By a proper normalization and a clever characterization, it is shown that the average squared error cost function for identification can be expressed in terms of the inner product between the true but unknown parameter vector and its estimate. Further, the cost function is concave in the inner product and linear in the inner product square. Therefore, the identification of parametric Hammerstein systems with FIR linear parts is a globally convergent problem and has one and only one (local and global) minimum. This implies that the identification of such systems is a linear problem in terms of the inner product square and any local search based identification algorithm converges globally. 相似文献