论文标题
使用基于内核的方法在动态网络中学习线性模块
Learning linear modules in a dynamic network using regularized kernel-based methods
论文作者
论文摘要
为了在一个互连的动态网络中识别一个系统（模块），通常必须求解一个多输入单位输出（MISO）标识问题，该问题需要识别Miso设置中的所有模块。为了应用参数识别方法，这将需要估计大量参数，以及可能大规模误差问题的适当模型订单选择步骤，从而将识别算法的计算复杂性提高到超出可行性的级别。采用基于正规内核的方法提出了一种替代识别方法。为感兴趣的模块保留参数模型，我们将味o结构中其余模块的脉冲响应建模为零平均高斯工艺（GP），并使用协方差矩阵（kernel）（kernel）由一阶稳定稳定的稳定样条线核内提供，从而考虑了影响目标模块的噪声模型以及可能会在误差设置中的噪声模型。使用经验贝叶斯（EB）方法，目标模块参数是通过预期最大化（EM）算法估算的，其计算复杂性大大降低，同时避免了广泛的模型结构选择。数值模拟说明了与局部模块识别的最新技术相比，引入方法的电势。
In order to identify one system (module) in an interconnected dynamic network, one typically has to solve a Multi-Input-Single-Output (MISO) identification problem that requires identification of all modules in the MISO setup. For application of a parametric identification method this would require estimating a large number of parameters, as well as an appropriate model order selection step for a possibly large scale MISO problem, thereby increasing the computational complexity of the identification algorithm to levels that are beyond feasibility. An alternative identification approach is presented employing regularized kernel-based methods. Keeping a parametric model for the module of interest, we model the impulse response of the remaining modules in the MISO structure as zero mean Gaussian processes (GP) with a covariance matrix (kernel) given by the first-order stable spline kernel, accounting for the noise model affecting the output of the target module and also for possible instability of systems in the MISO setup. Using an Empirical Bayes (EB) approach the target module parameters are estimated through an Expectation-Maximization (EM) algorithm with a substantially reduced computational complexity, while avoiding extensive model structure selection. Numerical simulations illustrate the potentials of the introduced method in comparison with the state-of-the-art techniques for local module identification.
