Dynamical systems identification using Gaussian process models with incorporated local models

Gaussian process (GP) models form an emerging methodology for modelling nonlinear dynamic systems which tries to overcome certain limitations inherent to traditional methods such as e.g. neural networks (ANN) or local model networks (LMN).The GP model seems promising for three reasons. First, less training parameters are needed to parameterize the model. Second, the variance of the model's output depending on data positioning is obtained. Third, prior knowledge, e.g. in the form of linear local models can be included into the model. In this paper the focus is on GP with incorporated local models as the approach which could replace local models network.Much of the effort up to now has been spent on the development of the methodology of the GP model with included local models, while no application and practical validation has yet been carried out. The aim of this paper is therefore twofold. The first aim is to present the methodology of the GP model identification with emphasis on the inclusion of the prior knowledge in the form of linear local models. The second aim is to demonstrate practically the use of the method on two higher order dynamical systems, one based on simulation and one based on measurement data.     

Stochastic system transformation using generalized orthonormal basis functions with applications to continuous-time system identification

This paper studies the system transformation using generalized orthonormal basis functions that include the Laguerre basis as a special case. The transformation of the deterministic systems is studied in the literature, which is called the Hambo transform. The aim of the paper is to develop a transformation theory for stochastic systems. The paper establishes the equivalence of continuous and transformed-discrete-time stochastic systems in terms of solutions. The method is applied to the continuous-time system identification problem. It is shown that using the transformed signals the PO-MOESP subspace identification algorithm yields consistent estimates for system matrices. An example is included to illustrate the efficacy of the proposed identification method, and to make a comparison with the method using the Laguerre filter.     

高斯光束通过空间滤波器的传输特性

采用矩阵分解和用有限个复高斯函数之和逼近硬边光阑窗口函数相结合的方法,从柯林斯衍射积分公式出发,对高斯光束通过空间滤波器的传输特性进行了分析,得到了相应的解析式。通过对解析式的数值模拟,得出了直观的结果,并进行了误差分析。数值计算结果表明,最佳滤波小孔尺寸的选择和计算误差均与光束的截断参数有关。在此基础上,进一步研究了空间滤波器具有像传递功能时,高斯光束和平顶高斯光束(N=12)通过空间滤波器的传输特性,得出了截断参数与滤波后光强分布的关系,并证实了此方法也可用于计算其他类型光束通过空间滤波器的传输。     

傍轴黎曼几何光学Ⅲ．光束传输的统计行为

利用厄米－高斯展开方法，讨论了傍轴光束传输的统计行为，得到了与传输统计行为等效的高斯光束的传输参数，这些结果可用于傍轴光束传输的几何化描写。     

高斯光束在介质波导中的传播

本文考虑轴对称高斯型激光束在均匀介质及类透镜介质中的传播问题。本文采用作者以前所导出的正确方程,计及光束的衍射效应,求出了方程的解析解。在此基础上,讨论了激光束的宽度和波阵面问题。在均匀介质中,激光束将单调变粗;在类透镜介质中,激光束的宽度是z正弦函数,粗细交替出现。     

LDPC一致校验矩阵的LU分解算法

不同于Turbo码,LDPC是数学结构非常好的一类高速码。对其结构进行系统研究,不但对实际编码方案很有帮助,而且也有很高的理论价值。二进制低密度码（MN码）编码的基础为Gaussian消元法（消去法）。丈中推导了MN码的一致校验矩阵的基于初等变换的Gaussian消元法。研究表明,基于初等变换的Gaussian消元法结合“minprod”算法在进行LU分解时,可以有效地保持MN码一致校验矩阵的稀疏性。     

IRA码在体全息存储系统中的应用研究

在全息存储系统中,噪声分布是不均匀的,为了对存储的信息位进行不等保护,文中采用能纠不均匀错误的IRA码为系统的纠错码。通过高斯估计方法设计出适合全息存储系统的IRA码,并对其在系统中的应用进行了仿真,结果表明全息信道中IRA码的性能要远优于RS码。     

有源激光敌我识别系统作用距离仿真

分析了有源激光敌我识别系统组成和工作原理;在建立大气衰减模型基础上,利用高斯光束理论描述激光光束的传拎,从誓光峰值功率、束散角等参数推导出激光光束传输的理论模型,在考虑安装平台尺寸以及系统瞄准性能的前提下,对有源激光敌霉识别鎏鱼的佳用雌壹进行了仿真计算,计算结果表明最小作用距离对系统的限制较大,是设计激光敌我识别系统激...