## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|ECE310

statistics-lab™ 为您的留学生涯保驾护航 在代写信号处理与线性系统Signal Processing and Linear Systems方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写信号处理与线性系统Signal Processing and Linear Systems方面经验极为丰富，各种代写信号处理与线性系统Signal Processing and Linear Systems相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|Realization formulas

To present the Stieltjes-class analog of Theorem 1.4, we start with the collection
$$\Lambda={\mu, \mathcal{\mathcal { X }}, \tilde{\mathcal{X}}, \widehat{\mathcal{G}}, A, \widetilde{A}, B, C, \Pi, \widetilde{\Pi}}$$
consisting of a point $\mu \in \mathbb{C}$, three Hilbert spaces $\mathcal{X}, \tilde{\mathcal{X}}, \widehat{\mathcal{G}}=\mathcal{G} \oplus \mathcal{G}$, and bounded operators
\begin{aligned} &A \in \mathcal{L}(\mathcal{X}), \quad \tilde{A} \in \mathcal{L}(\tilde{\mathcal{X}}), \quad B \in \mathcal{L}(\tilde{\mathcal{X}}, \mathcal{X}), \quad C \in \mathcal{L}(\mathcal{X}, \tilde{\mathcal{X}}), \ &\Pi=\left[\begin{array}{l} \Pi_{1} \ \Pi_{2} \end{array}\right] \in \mathcal{L}(\mathcal{X}, \widehat{\mathcal{G}}), \quad \tilde{\Pi}=\left[\begin{array}{c} \widetilde{\Pi}{1} \ \widetilde{\Pi}{2} \end{array}\right] \in \mathcal{L}(\tilde{\mathcal{X}}, \widehat{\mathcal{G}}) \end{aligned}
and we call this collection admissible if the pairs $(\Pi, A)$ and $(\tilde{\Pi}, \tilde{A})$ are observable and the following equalities hold:
\begin{aligned} &A(I+\mu A)=B C, \quad \tilde{A}(I+\mu \tilde{A})=C B, \quad C A=\tilde{A} C, \quad A B=B \tilde{A} \ &\Pi_{1}\left[\begin{array}{ll} I+\mu A & B \end{array}\right]=\widetilde{\Pi}{1}\left[\begin{array}{ll} C & \tilde{A} \end{array}\right], \quad \Pi{2}\left[\begin{array}{ll} A & B \end{array}\right]=\widetilde{\Pi}_{2}\left[\begin{array}{ll} C & I+\mu \tilde{A} \end{array}\right] . \end{aligned}
As a model for an admissible collection, consider the choice based on a $\mathcal{L}(\widehat{\mathcal{G}})$-valued function $\Theta$ meromorphic on the domain $\Omega$ and a fixed point $\mu$ in $\Omega$ where $\Theta$ is analytic:
$\mathcal{X}=\mathcal{H}(\Theta), \quad \tilde{\mathcal{X}}=\mathcal{H}\left(\Theta_{P}\right), \quad A=\left.R_{\mu}\right|{\mathcal{H}(\Theta)}, \quad \widetilde{A}=\left.R{\mu}\right|{\mathcal{H}\left(\Theta{P}\right)}$,
$B=\left.\left[\begin{array}{cc}R_{\mu} & 0 \ 0 & I+\mu R_{\mu}\end{array}\right]\right|{\mathcal{H}\left(\Theta{P}\right)}, \quad C=\left.\left[\begin{array}{cc}I+\mu R_{\mu} & 0 \ 0 & R_{\mu}\end{array}\right]\right|{\mathcal{H}(\Theta)}$, $\Pi=E{\mu}\left|\mathcal{H}(\Theta), \quad \tilde{\Pi}=E_{\mu}\right|{\mathcal{H}\left(\Theta{P}\right)} .$
It is a consequence of Theorem $3.1$ that the mapping properties (4.2) work out with this specification. The remaining identities (4.3)-(4.4) follow from the definitions or straightforward algebra.
We will say that the collection (4.1) is similar to the collection
$$\Lambda=\left{\mu, \mathcal{X}^{\prime}, \widetilde{\mathcal{X}}^{\prime}, \widehat{\mathcal{G}}^{\prime}, A^{\prime}, \widetilde{A}^{\prime}, B^{\prime}, C^{\prime}, \Pi^{\prime}, \widetilde{\Pi}^{\prime}\right}$$
if there exist invertible operators $T \in \mathcal{L}\left(\mathcal{X}, \mathcal{X}^{\prime}\right)$ and $\widetilde{T} \in \mathcal{L}\left(\tilde{\mathcal{X}}, \tilde{\mathcal{X}}^{\prime}\right)$ such that $A^{\prime} T=T A, \quad \widetilde{A}^{\prime} \tilde{T}=\widetilde{T} \tilde{A}, \quad B^{\prime} \tilde{T}=T B, \quad C^{\prime} T=\tilde{T} C, \quad \Pi^{\prime} T=\Pi, \quad \widetilde{\Pi}^{\prime} \tilde{T}=\widetilde{\Pi} .$

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|Explicit formulas for Θ

Let us assume now that the gramians $\mathcal{G}{\Pi, A, \mu}$ and $\mathcal{G}{\tilde{\Pi}, \tilde{A}, \mu}$ are invertible. By the geneneral princíples of reproducíng kernèl Hilbert spaces, it follows from the reepresentations (4.5) that reproducing kernels $K_{\Theta}$ and $K_{P \Theta P^{-1}}$ for $\mathcal{H}$ and $\mathcal{H}$ are equal to
$$K_{P \Theta P^{-1}}(z, \omega)=\frac{J-\Theta_{P}(z) J \Theta_{P}(\omega)^{}}{i(\bar{\omega}-z)}=\left[\begin{array}{l} \widetilde{\Pi}{1} \ \widetilde{\Pi}{2} \end{array}\right] \widetilde{\Gamma}(z) \mathcal{G}{\widetilde{\Pi}, \widetilde{A}, \mu}^{-1} \widetilde{\Gamma}(\omega)^{}\left[\begin{array}{ll} \widetilde{\Pi}{1}^{} & \widetilde{\Pi}_{2}^{} \end{array}\right]$$
The next question is to find a fairly satisfactory formula for $\Theta$ satisfying the kernel identities (4.35), (4.36).

Theorem 4.5. Given an admissible collection (4.1) with $\mu \in \mathbb{R}$ and subject to the identity (4.30). Then:

1. The functions
\begin{aligned} &\Upsilon(z)=I_{\widehat{\mathcal{G}}}+i(z-\mu) \Pi \Gamma(z) \mathcal{G}{\Pi, A, \mu}^{-1} \Pi^{} J, \ &\widetilde{\Upsilon}(z)=I{\widehat{\mathcal{G}}}+i(z-\mu) \widetilde{\Pi} \widetilde{\Gamma}(z) \mathcal{G}{\widetilde{\Pi}, \widetilde{A}, \mu}^{-1} \widetilde{\Pi}^{} J \end{aligned}
belong to the class $\mathcal{M \mathcal { P }}(\mathcal{G})$ and the kernels $K{\Upsilon}(z, \omega)$ and $K_{\tilde{\Upsilon}}(z, \omega)$ are equal to the right-hand side expressions in (4.35), (4.36):
\begin{aligned} &K_{\curlyvee}(z, \omega)=\left[\begin{array}{l} \Pi_{1} \ \Pi_{2} \end{array}\right] \Gamma(z) \mathcal{G}{\Pi, A, \mu}^{-1} \Gamma(\omega)^{}\left[\begin{array}{ll} \Pi{1}^{} & \Pi_{2}^{} \end{array}\right], \ &K_{\widetilde{\Upsilon}}(z, \omega)=\left[\begin{array}{l} \widetilde{\Pi}{1} \ \widetilde{\Pi}{2} \end{array}\right] \widetilde{\Gamma}(z) \mathcal{G}{\widetilde{\Pi}, \widetilde{A}, \mu}^{-1} \widetilde{\Gamma}(\omega)^{}\left[\begin{array}{ll} \widetilde{\Pi}{1}^{} & \widetilde{\Pi}_{2}^{} \end{array}\right] . \end{aligned}
2. Furthermore, there exist J-unitary operators $N, \widetilde{N} \in \mathcal{L}(\widehat{\mathcal{G}})$ such that the function $\Theta(z)=\Upsilon(z) N$ belongs to the class $\mathcal{M S}(\mathcal{G})$ and the associated function $\Theta_{P}$ is equal to $\Theta_{P}(z):=P(z) \Theta(z) P(z)^{-1}=\tilde{\Upsilon}(z) \bar{N}$.

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|Realization formulas

$$\Lambda=\mu, \mathcal{X}, \tilde{\mathcal{X}}, \widehat{\mathcal{G}}, A, \widetilde{A}, B, C, \Pi, \widetilde{\Pi}$$

$A \in \mathcal{L}(\mathcal{X}), \quad \tilde{A} \in \mathcal{L}(\tilde{\mathcal{X}}), \quad B \in \mathcal{L}(\tilde{\mathcal{X}}, \mathcal{X}), \quad C \in \mathcal{L}(\mathcal{X}, \tilde{\mathcal{X}}), \quad \Pi=\left[\Pi_{1} \Pi_{2}\right] \in \mathcal{L}(\mathcal{X}, \widehat{\mathcal{G}}), \quad \tilde{\Pi}=[\tilde{\Pi}$

$$A(I+\mu A)=B C, \quad \tilde{A}(I+\mu \tilde{A})=C B, \quad C A=\tilde{A} C, \quad A B=B \tilde{A} \quad \Pi_{1}[I+\mu A \quad B]=\widetilde{\Pi} 1[C$$

$$\mathcal{X}=\mathcal{H}(\Theta), \quad \tilde{\mathcal{X}}=\mathcal{H}\left(\Theta_{P}\right), \quad A=R_{\mu}|\mathcal{H}(\Theta), \quad \widetilde{A}=R \mu| \mathcal{H}(\Theta P) \text {, }$$
$B=\left[\begin{array}{llll}R_{\mu} & 0 & 0 & I+\mu R_{\mu}\end{array}\right]\left|\mathcal{H}(\Theta P), \quad C=\left[I+\mu R_{\mu} \quad 0 \quad 0 \quad R_{\mu}\right]\right| \mathcal{H}(\Theta)$,
$\Pi=E \mu\left|\mathcal{H}(\Theta), \quad \tilde{\Pi}=E_{\mu}\right| \mathcal{H}(\Theta P)$.

$A^{\prime} T=T A, \quad \tilde{A}^{\prime} \tilde{T}=\widetilde{T} \tilde{A}, \quad B^{\prime} \tilde{T}=T B, \quad C^{\prime} T=\tilde{T} C, \quad \Pi^{\prime} T=\Pi, \quad \tilde{\Pi}^{\prime} \tilde{T}=\widetilde{\Pi} .$

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|Explicit formulas for Θ

$$K_{P \Theta P^{-1}}(z, \omega)=\frac{J-\Theta_{P}(z) J \Theta_{P}(\omega)}{i(\bar{\omega}-z)}=[\widetilde{\Pi} 1 \widetilde{\Pi} 2] \widetilde{\Gamma}(z) \mathcal{G} \widetilde{\Pi}, \widetilde{A}, \mu^{-1} \widetilde{\Gamma}(\omega)\left[\widetilde{\Pi} 1 \quad \widetilde{\Pi}_{2}\right]$$

1. 功能
$$\Upsilon(z)=I_{\widehat{\mathcal{G}}}+i(z-\mu) \Pi \Gamma(z) \mathcal{G} \Pi, A, \mu^{-1} \Pi J, \quad \widetilde{\Upsilon}(z)=I \widehat{\mathcal{G}}+i(z-\mu) \widetilde{\Pi} \widetilde{\Gamma}(z) \mathcal{G} \widetilde{\Pi}, \widetilde{A}, \mu^{-1} \widetilde{\Pi} J$$
属于类 $\mathcal{M} \mathcal{P}(\mathcal{G})$ 和内核 $K \Upsilon(z, \omega)$ 和 $K_{\tilde{\Upsilon}}(z, \omega)$ 等于 (4.35), (4.36) 中的右侧表达式:
$$K_{\curlyvee}(z, \omega)=\left[\Pi_{1} \Pi_{2}\right] \Gamma(z) \mathcal{G} \Pi, A, \mu^{-1} \Gamma(\omega)\left[\begin{array}{ll} \Pi 1 & \Pi_{2} \end{array}\right], \quad K_{\tilde{\Upsilon}}(z, \omega)=[\widetilde{\Pi} 1 \widetilde{\Pi} 2] \widetilde{\Gamma}(z) \mathcal{G} \widetilde{\Pi}, \widetilde{A}, \mu^{-1}$$
2. 此外，存在J-酉算子 $N, \widetilde{N} \in \mathcal{L}(\widehat{\mathcal{G}})$ 使得函数 $\Theta(z)=\Upsilon(z) N$ 属于类 $\mathcal{M} \mathcal{S}(\mathcal{G})$ 和相关的功能 $\Theta_{P}$ 等于 $\Theta_{P}(z):=P(z) \Theta(z) P(z)^{-1}=\tilde{\Upsilon}(z) \bar{N}$

## 广义线性模型代考

statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|EE483

statistics-lab™ 为您的留学生涯保驾护航 在代写信号处理与线性系统Signal Processing and Linear Systems方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写信号处理与线性系统Signal Processing and Linear Systems方面经验极为丰富，各种代写信号处理与线性系统Signal Processing and Linear Systems相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|The focus here

However our focus here is not on interpolation aspects but rather on the intrinsic structure of the associated reproducing kernel Hilbert spaces. The main objective of the present paper is to find Stieltjes-class counterparts of Theorems $1.3$ and 1.4. Specifically, in Section 3 we shall consider the following:

Problem 1.10. Given two reproducing kernel Hilbert spaces $\mathcal{H}$ and $\tilde{\mathcal{H}}$ of $\widehat{\mathcal{G}}$-valued functions meromorphic in $\Omega$, find necessary and sufficient conditions for the existence of a function $\Theta \in \mathcal{M S}(\mathcal{G}, \Omega)$ such that $\mathcal{H}=\mathcal{H}(\Theta)$ and $\widetilde{\mathcal{H}}=\mathcal{H}\left(P \Theta P^{-1}\right)$. In case $\mathcal{H}$ and $\widetilde{\mathcal{H}}$ are presented as ranges of observability operators
$$\mathcal{H}=\operatorname{Ran} \mathcal{O}{\Pi, A, \mu} \quad \text { and } \quad \tilde{\mathcal{H}}=\operatorname{Ran} \mathcal{O}{\tilde{\Pi}, \tilde{A}, \mu},$$ find necessary and sufficient conditions directly in terms of the operators $\Pi, A, \widetilde{\Pi}, \widetilde{A}$ for it to happen that $\mathcal{H}=\mathcal{H}(\Theta)$ and $\tilde{\mathcal{H}}=\mathcal{H}\left(\Theta_{P}\right)$ for some $\Theta$.

Solutions to these problems are presented in Theorem $3.1$ (the Stieltjes analogue of Theorem 1.3) and Theorem $4.1$ (the Stieltjes analogue of Theorem 1.4).
Finally we note that the reproducing kernel space $\mathcal{H}(\Theta)$ determines the function $\Theta \in \mathcal{M} \mathcal{P}(\mathcal{G}, \Omega)$ only up to a unitary constant right factor $\Upsilon$. While $\Theta \Upsilon$ is in the Pick class $\mathcal{M} \mathcal{P}(\mathcal{G}, \Omega)$ whenever $\Theta \in \mathcal{M} \mathcal{P}(\mathcal{G}, \Omega)$ for any constant $J$-unitary operator $\Upsilon$, the corresponding property for the multiplicative Stieltjes class fails in general. Thus it is a subtle but nontrivial point to show that, if $\Theta$ is such that $\mathcal{H}=\mathcal{H}(\Theta)$ and $\widetilde{\mathcal{H}}=\mathcal{H}\left(\Theta_{P}\right)$, then there is a choice of constant J-unitary operators $\Upsilon$ and $\tilde{\Upsilon}$ so that $(\Theta \cdot \Upsilon){P}=\Theta{P} \cdot \widetilde{\Upsilon}$, in which case we then have $\Theta^{\prime}:=\Theta \cdot \Upsilon \in \mathcal{M} \mathcal{S}(\mathcal{G}, \Omega)$ as well as $\mathcal{H}=\mathcal{H}\left(\Theta^{\prime}\right)$ and $\tilde{\mathcal{H}}=\mathcal{H}\left(\left(\Theta^{\prime}\right)_{P}\right)$. This issue is addressed in Section $4.2$ below.

The paper is organized as follows. Section 2 presents some material on the simultaneous $J$-unitary equivalence of a pair of Krein-space operators as well as some identities involving the operators $R_{\alpha}$ and $\left[\begin{array}{cc}R_{\alpha} & 0 \ 0 & I+\alpha R_{\alpha}\end{array}\right]$ needed in the proof of the characterization of a pair of reproducing kernel Hilbert spaces of the form $\mathcal{H}(\Theta)$ and $\mathcal{H}\left(\Theta_{P}\right)$. Section 3 gives an intrinsic structural characterization of pairs of reproducing kernel Hilbert spaces of the form $\left(\mathcal{H}(\Theta), \mathcal{H}\left(\Theta_{P}\right)\right)$ in intrinsic geometric, structural form, while in Section 4, these results are reformulated in explicit state-space coordinates.

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|Characterization of Stieltjes reproducing-kernel

In this section we characterize pairs $\left{\mathcal{H}(\Theta), \mathcal{H}\left(P \Theta P^{-1}\right)\right}$ in terms of invariance properties and structure identities.

Theorem 3.1. Let $\mathcal{H}$ and $\widetilde{\mathcal{H}}$ be two reproducing kernel Hilbert spaces whose elements are $\widehat{\mathcal{G}}$ valued functions which are meromorphic in $\Omega$. In order that $\mathcal{H}$ and $\widetilde{\mathcal{H}}$ be spaces $\mathcal{H}(\Theta)$ and $\mathcal{H}\left(P \Theta P^{-1}\right)$ it is necessary and sufficient that

1. For each $\alpha \in \Omega$, the invariance conditions
$$R_{\alpha} \mathcal{H} \subset \mathcal{H}, \quad R_{\alpha} \tilde{\mathcal{H}} \subset \widetilde{\mathcal{H}}$$
hold as well as the coupled invariance conditions
$$\left[\begin{array}{cc} I+\alpha R_{\alpha} & 0 \ 0 & R_{\alpha} \end{array}\right] \mathcal{H} \subset \tilde{\mathcal{H}} \text { and }\left[\begin{array}{cc} R_{\alpha} & 0 \ 0 & I+\alpha R_{\alpha} \end{array}\right] \tilde{\mathcal{H}} \subset \mathcal{H} .$$
2. The following four identities hold for all functions
3. $F=\left[\begin{array}{l}F_{1} \ F_{2}\end{array}\right] \in \mathcal{H}, \quad G=\left[\begin{array}{l}G_{1} \ G_{2}\end{array}\right] \in \mathcal{H}, \quad \widetilde{F}=\left[\begin{array}{c}\widetilde{F}{1} \ \widetilde{F}{2}\end{array}\right] \in \widetilde{\mathcal{H}}, \quad \widetilde{G}=\left[\begin{array}{c}\widetilde{G}{1} \ \widetilde{G}{2}\end{array}\right] \in \widetilde{\mathcal{H}}$
4. and for all $\alpha, \beta \in \Omega$ :
5. $\left\langle R_{\alpha} F,\left(I+\beta R_{\beta}\right) G\right\rangle_{\mathcal{H}}-\left\langle\left[\begin{array}{cc}I+\alpha R_{\alpha} & 0 \ 0 & R_{\alpha}\end{array}\right] F,\left[\begin{array}{cc}I+\beta R_{\beta} & 0 \ 0 & R_{\beta}\end{array}\right] G\right\rangle_{\tilde{\mathcal{H}}}$
6. $=G_{2}(\beta)^{} F_{1}(\alpha)$, $\left\langle\left[\begin{array}{cc}R_{\alpha} & 0 \ 0 & I+\alpha R_{\alpha}\end{array}\right] \widetilde{F},\left[\begin{array}{cc}R_{\beta} & 0 \ 0 & I+\beta R_{\beta}\end{array}\right] \widetilde{G}\right\rangle_{\mathcal{H}}-\left\langle\left(I+\alpha R_{\alpha}\right) \widetilde{F}, R_{\beta} \widetilde{G}\right\rangle_{\tilde{\mathcal{H}}}$ $=\widetilde{G}{2}(\beta)^{} \widetilde{F}{1}(\alpha)$,
7. $\left\langle\left[\begin{array}{cc}R_{\alpha} & 0 \ 0 & I+\alpha R_{\alpha}\end{array}\right] \widetilde{F}, R_{\beta} G\right\rangle_{\mathcal{H}}-\left\langle R_{\alpha} \widetilde{F},\left[\begin{array}{cc}I+\beta R_{\beta} & 0 \ 0 & R_{\beta}\end{array}\right] G\right\rangle_{\tilde{\mathcal{H}}}$
8. $=G_{1}(\beta)^{} \widetilde{F}{2}(\alpha)$, $\left\langle\left[\begin{array}{cc}R{\alpha} & 0 \ 0 & I+\alpha R_{\alpha}\end{array}\right] \widetilde{F},\left(I+\beta R_{\beta}\right) G\right\rangle_{\varkappa}-\left\langle\left(I+\alpha R_{\alpha}\right) \widetilde{F},\left[\begin{array}{cc}I+\beta R_{\beta} & 0 \ 0 & R_{\beta}\end{array}\right] G\right\rangle_{\tilde{\varkappa}}$
9. $=G_{2}(\beta)^{} \widetilde{F}_{1}(\alpha)$

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|Characterization of Stieltjes reproducing-kernel

1. 对于每个 $\alpha \in \Omega$, 不变条件
$$R_{\alpha} \mathcal{H} \subset \mathcal{H}, \quad R_{\alpha} \tilde{\mathcal{H}} \subset \widetilde{\mathcal{H}}$$
保持以及耦合不变条件
$$\left[\begin{array}{llll} I+\alpha R_{\alpha} & 0 & 0 & R_{\alpha} \end{array}\right] \mathcal{H} \subset \tilde{\mathcal{H}} \text { and }\left[\begin{array}{llll} R_{\alpha} & 0 & 0 & I+\alpha R_{\alpha} \end{array}\right] \tilde{\mathcal{H}} \subset \mathcal{H} .$$
2. 以下四个恒等式适用于所有功能
3. $F=\left[\begin{array}{ll}F_{1} & F_{2}\end{array}\right] \in \mathcal{H}, \quad G=\left[\begin{array}{ll}G_{1} & G_{2}\end{array}\right] \in \mathcal{H}, \quad \widetilde{F}=[\widetilde{F} 1 \widetilde{F} 2] \in \widetilde{\mathcal{H}}, \quad \widetilde{G}=[\widetilde{G} 1 \widetilde{G} 2] \in \widetilde{\mathcal{H}}$
4. 并为所有人 $\alpha, \beta \in \Omega$ :
5. $\left\langle R_{\alpha} F,\left(I+\beta R_{\beta}\right) G\right\rangle_{\mathcal{H}}-\left\langle\left[I+\alpha R_{\alpha} \quad 0 \quad 0 \quad R_{\alpha}\right] F,\left[I+\beta R_{\beta} \quad 0 \quad 0 \quad R_{\beta}\right] G\right\rangle_{\tilde{\mathcal{H}}}$
6. $=G_{2}(\beta) F_{1}(\alpha)$,
$\left\langle\left[\begin{array}{llll}R_{\alpha} & 0 & 0 & I+\alpha R_{\alpha}\end{array}\right] \widetilde{F},\left[\begin{array}{llll}R_{\beta} & 00 & I+\beta R_{\beta}\end{array}\right] \widetilde{G}\right\rangle_{\mathcal{H}}-\left\langle\left(I+\alpha R_{\alpha}\right) \widetilde{F}, R_{\beta} \widetilde{G}\right\rangle_{\tilde{\mathcal{H}}}$ $=\widetilde{G} 2(\beta) \widetilde{F} 1(\alpha)$,
7. $\left\langle\left[\begin{array}{llll}R_{\alpha} & 0 & 0 & I+\alpha R_{\alpha}\end{array}\right] \widetilde{F}, R_{\beta} G\right\rangle_{\mathcal{H}}-\left\langle R_{\alpha} \widetilde{F},\left[I+\beta R_{\beta} \quad 00 \quad R_{\beta}\right] G\right\rangle_{\tilde{\mathcal{H}}}$
8. $=G_{1}(\beta) \widetilde{F} 2(\alpha)$, 9. $=G_{2}(\beta) \widetilde{F}_{1}(\alpha)$

## 广义线性模型代考

statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|ELEN30012

statistics-lab™ 为您的留学生涯保驾护航 在代写信号处理与线性系统Signal Processing and Linear Systems方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写信号处理与线性系统Signal Processing and Linear Systems方面经验极为丰富，各种代写信号处理与线性系统Signal Processing and Linear Systems相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|The Stieltjes and multiplicative Stieltjes classes

An important subclass of the Pick class is the Stieltjes class denoted here by $\mathcal{S}(\widehat{\mathcal{G}})$, consisting of functions $S$ in the Pick class $\mathcal{P}(\widehat{\mathcal{G}})$ with analytic continuation across the negative half-axis $\mathbb{R}^{-}$and taking positive semidefinite values on $\mathbb{R}^{-}$:
$$\frac{S(z)-S(z)^{}}{z-\bar{z}} \succeq 0(z \notin \mathbb{R}), \quad S(x) \succeq 0 \quad(x<0) .$$ Stieltjes functions made their first explicit appearance in [44] as continued fractions of certain type and as Cauchy transforms of positive measures on $\mathbb{R}^{+}=[0, \infty)$. Being special instances of absolutely monotone functions, operator monotone functions and Pick functions, they have been extensively studied in various contexts $[12,29,30,34,33,37,43,45]$. Such functions have the alternative characterization as being those functions $S \in \mathcal{P}(\widehat{\mathcal{G}})$ such that the function $z \mapsto z S(z)$ is also in $\mathcal{P}(\widehat{\mathcal{G}})$ (see [33] for the scalar case – the operator-valued case is similar). This leads to the kernel characterization of the Stieltjes class: an $\mathcal{L}(\mathcal{G})$-valued function $S$ is in $\mathcal{S}(\mathcal{G})$ if and only if both kernels $$\mathfrak{K}(z, \omega)=\frac{S(z)-S(\omega)^{}}{z-\bar{\omega}} \text { and } \widetilde{\mathfrak{K}}(z, \omega)=\frac{z S(z)-\bar{\omega} S(\omega)^{*}}{z-\bar{\omega}}$$
are positive on the upper half-plane.

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|Connections with interpolation theory

The importance of multiplicative Pick functions for interpolation theory arises from the fact that the linear fractional map based on a function $\Theta \in \mathcal{M} \mathcal{P}(\widehat{\mathcal{G}})$ maps the class $\mathcal{P}(\mathcal{G})$ into itself. Choosing $\Theta$ with a suitable pole/zero structure then implies that the linear-fractional map based on $\Theta$ gives rise to a parametrization (with free parameter from the Pick class $\mathcal{P}(\mathcal{G})$ ) of the solution set of a given interpolation problem in the class $\mathcal{P}(\mathcal{G})$; we refer to $[11,42]$ for specific examples. It turns out the multiplicative Stieltjes class $\mathcal{M S}(\mathcal{G}, \mathbb{C})$ has similar applications in interpolation theory for the additive Stieltjes class $\mathcal{S}(\mathcal{G})$ as the linear fractional map based on a function $\Theta \in \mathcal{M} \mathcal{S}(\mathcal{G})$ not only maps the class $\mathcal{P}(\mathcal{G})$ into itself, but also the class $\mathcal{S}(\mathcal{G})$ into itself. In the context of the Nevanlinna-Pick interpolation problem, multiplicative Stieltjes functions appeared explicitly in the series of papers $[23,25,26]$; see also $[2,13,14,15,24,25,26]$ for other examples and far-reaching generalizations. From the integral representation (1.27) for the Stieltjes class, we see that the Stieltjes moment problem going back to the nineteenth century [44] can be seen as a boundary version of a Stieltjes interpolation problem. The Stieltjes class also arises in the recent work of Agler-Tully-Doyle-Young [1] on characterizing boundary directional derivatives of Schur-class functions on the bidisk.

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|The Stieltjes and multiplicative Stieltjes classes

Pick 类的一个重要子类是 Stieltjes 类，在此表示为 $S(\widehat{\mathcal{G}})$ ，由函数组成 $S$ 在 Pick 类中 $\mathcal{P}(\widehat{\mathcal{G}})$ 在负半轴上具有解析延拓 $\mathbb{R}^{-}$并取半正定值 $\mathbb{R}^{-}$:
$$\frac{S(z)-S(z)}{z-\bar{z}} \succeq 0(z \notin \mathbb{R}), \quad S(x) \succeq 0 \quad(x<0) .$$
Stieltjes 函数在 [44] 中作为某种类型的连分数和正测量的柯西变换在 [44] 中首次明确出现 $\mathbb{R}^{+}=[0, \infty)$. 作为绝 对单调函数、算子单调函数和 Pick 函数的特例，它们在各种情况下得到了广泛的研究
$[12,29,30,34,33,37,43,45]$. 此类功能具有作为这些功能的替代特征 $S \in \mathcal{P}(\widehat{\mathcal{G}})$ 使得函数 $z \mapsto z S(z)$ 也在 $\mathcal{P}(\widehat{\mathcal{G}})$ (有关标量情况，请参见 [33] – 运算符值情况类似) 。这导致了 Stieltjes 类的内核特征: $\mathcal{L}(\mathcal{G})$ 值函数 $S$ 在 $\mathcal{S}(\mathcal{G})$ 当且仅当两个内核
$$\mathfrak{K}(z, \omega)=\frac{S(z)-S(\omega)}{z-\bar{\omega}} \text { and } \widetilde{\Re}(z, \omega)=\frac{z S(z)-\bar{\omega} S(\omega)^{*}}{z-\bar{\omega}}$$

## 广义线性模型代考

statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|ELECENG4112

statistics-lab™ 为您的留学生涯保驾护航 在代写信号处理与线性系统Signal Processing and Linear Systems方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写信号处理与线性系统Signal Processing and Linear Systems方面经验极为丰富，各种代写信号处理与线性系统Signal Processing and Linear Systems相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|Reproducing kernel Hilbert spaces with additional structure

In this paper we shall be interested in how additional properties of the positive kernel $K$ translate to additional structural properties of the reproducing kernel Hilbert space $\mathcal{H}{K}$. A specific form for the positive kernel $K$ of interest for us can be explained as follows. Given a Hilbert space $\mathcal{G}$, we define the unitary selfadjoint operator $$J=\left[\begin{array}{cc} 0 & i I{\mathcal{G}} \ -i I_{\mathcal{G}} & 0 \end{array}\right] \in \mathcal{L}(\mathcal{G} \oplus \mathcal{G})$$
To distinguish the summands in the direct sum $\widehat{\mathcal{G}}=\mathcal{G} \oplus \mathcal{G}$, we identify the first summand with the subspace $\mathcal{G}=\left{\left[\begin{array}{c}x \ 0\end{array}\right], x \in \mathcal{G}\right}$ of $\widehat{\mathcal{G}}$ and represent $\widehat{\mathcal{G}}$ as
$$\widehat{\mathcal{G}}=\mathcal{G} \oplus J \mathcal{G} .$$
We choose and fix a non-empty open subset $\Omega \subset \mathbb{C}$ which is symmetric about the real axis $\mathbb{R}$ and consider a Hilbert space $\mathcal{H}$ whose elements are $\widehat{\mathcal{G}}$-valued functions meromorphic in $\Omega$. Any reference to the value of a meromorphic function at $\alpha \in \Omega$ assumes that the function is analytic at $\alpha$.

Definition 1.2. We say that $\mathcal{H}$ is a space $\mathcal{H}(\Theta)$ if it admits a reproducing kernel $K_{\Theta}$ of the form
$$K_{\Theta}(z, \omega):=\frac{J-\Theta(z) J \Theta(\omega)^{}}{i(\bar{\omega}-z)}$$ for some function $\Theta$ meromorphic on $\Omega$, subject to $$\Theta(z) J \Theta(\bar{z})^{}=\Theta(\bar{z})^{*} J \Theta(z)=J \quad \text { for all } \quad z \in \Omega,$$ i.e., if H = HKΘ =: H(Θ) where KΘ is as in (1.4)–(1.5).

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|The Pick class and connections

Let us recall the Pick class $\mathcal{P}(\mathcal{G})$ (in the literature also known as NevanlinnaHerglotz class and sometimes also simply as $R$-class) consisting of $\mathcal{L}(\mathcal{G})$-valued functions holomorphic on the upper half-plane $\mathbb{C}{+}$with values there having positive semidefinite imaginary part, i.e., the functions $S: \mathbb{C}{+} \rightarrow \mathcal{L}(\mathcal{G})$ such that the kernel
$$\mathfrak{K}{S}(z, \omega)=\frac{S(z)-S(\omega)^{}}{z-\bar{\omega}}$$ is positive on $\mathbb{C}{+} .$In fact, if the kernel (1.18) is positive on a domain $\Omega \subset \mathbb{C}{+}$, it can be (uniquely) extended as a positive kernel to all of $\mathbb{C}{+}$due to the Pick interpolation theorem. It is convenient (and is consistent with Nevanlinna-Herglotz integral formula) furthermore to extend Pick functions to the lower half-plane by reflection: define $S(z)=S(\bar{z})^{}$ for $z \in \mathbb{C}^{-}$.

Let us note that the kernel $\mathfrak{K}{S}$ can be rewritten in a more aggregate form as \begin{aligned} \mathfrak{K}{S}(z, \bar{\omega}) &=\frac{\left[\begin{array}{ll} I & S(z) \end{array}\right]\left[\begin{array}{cc} 0 & i I_{\mathcal{G}} \ -i I_{\mathcal{G}} & 0 \end{array}\right]\left[\begin{array}{c} I \ S(\omega)^{} \end{array}\right]}{i(\bar{\omega}-z)} \ &=\frac{\left[\begin{array}{ll} I & S(z) \end{array}\right] \mathcal{J}{\mathcal{P}}\left[\begin{array}{c} I \ S(\omega)^{} \end{array}\right]}{i(\bar{\omega}-z)}, \quad \text { where } \quad \mathcal{J}{\mathcal{P}}=\left[\begin{array}{cc} 0 & i I_{\widehat{\mathcal{G}}} \ i I_{\widehat{\mathcal{G}}} & 0 \end{array}\right] . \end{aligned}
In case we replace $\mathcal{G}$ with $\widehat{\mathcal{G}}=\mathcal{G} \oplus J \mathcal{G}$, comparison of (1.19) with (1.6) suggests the close connection between the multiplicative Pick class $\mathcal{M} \mathcal{P}(\mathcal{G})$ and the Pick class over $\widehat{\mathcal{G}}$, i.e., $\mathcal{P}(\widehat{\mathcal{G}})$; the kernel $K_{\Theta}$ built from $\Theta$ appearing in (1.6) has exactly the same form as the kernel $\mathfrak{K}{S}$ built from $S$ appearing in (1.19), but with the aggregate signature matrix $\mathcal{J}{\mathcal{M} \mathcal{P}}$ for the class $\mathcal{M} \mathcal{P}(\mathcal{G})$ replaced by the aggregate signature matrix $\mathcal{J}{\mathcal{P}}$ for the class $\mathcal{P}(\widehat{\mathcal{G}})$. In fact there is a simple linear-fractional transformation $T{\mathcal{P G}}$ (called the Potapov-Ginzburg transformation (see [27]) which maps $\mathcal{P}(\widehat{\mathcal{G}})$ bijectively to $\mathcal{M P}(\mathcal{G})$ and which can be derived as follows.

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|Reproducing kernel Hilbert spaces with additional structure

$$J=\left[\begin{array}{lll} 0 & i I \mathcal{G}-i I_{\mathcal{G}} & 0 \end{array}\right] \in \mathcal{L}(\mathcal{G} \oplus \mathcal{G})$$

$$\widehat{\mathcal{G}}=\mathcal{G} \oplus J \mathcal{G} .$$

$$K_{\Theta}(z, \omega):=\frac{J-\Theta(z) J \Theta(\omega)}{i(\bar{\omega}-z)}$$

$$\Theta(z) J \Theta(\bar{z})=\Theta(\bar{z})^{*} J \Theta(z)=J \quad \text { for all } \quad z \in \Omega,$$

## 电子工程代写|信号处理与线性系统作业代写Signal Processing and Linear Systems代考|The Pick class and connections

$$\mathfrak{K} S(z, \omega)=\frac{S(z)-S(\omega)}{z-\bar{\omega}}$$

$\mathbb{C}+$ 由于 Pick 揷值定理。此外，通过反射将 Pick 函数扩展到下半平面很方便 (并且与 Nevanlinna-Herglotz 积分 公式一致)：定义 $S(z)=S(\bar{z})$ 为了 $z \in \mathbb{C}^{-}$.

## 广义线性模型代考

statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。