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SJTU Signal&System 期末整理

2023-07-28 01:28 作者:Elittocs  | 我要投稿

1 基本概念

系统的性质(system properties):(1)记忆系统(memory system):输入包含当前时刻之外的其他时刻;无记忆系统(memoryless system):输入仅与当前时刻相关;(2)因果系统(casual system):输出只取决于当前和该时刻之前的输入;(3)稳定性(stability):%5Csum_%7Bk%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%20%7B%7Ch%5Bn%5D%7C%20%3C%20%5Cinfty%7D%20 ,%5Cint_%7B-%5Cinfty%7D%5E%7B%5Cinfty%7D%20%7Ch(t)%7Cdt%20%3C%20%5Cinfty;(4)时变性(time invariance):系统自身性质与时间是否相关(判断方法:对信号先时移再变换与先变换再时移是否相等)(5)线性性(linearity):判断方法,已知 x(t)%20%5Crightarrow%20y(t) , 计算 ax_1(t)%2Bbx_2(t)%20%5Crightarrow%20ay_1(t)%20%2B%20by_2(t)是否成立

2 求解卷积(convolution)

Convolution Sum

y%5Bn%5D%20%3D%20...%2Bx%5B-1%5Dh%5Bn%2B1%5D%2Bx%5B0%5Dh%5Bn%5D%2Bx%5B1%5Dh%5Bn-1%5D%2B...%3D%5Csum_%7Bk%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%20%7Bx%5Bk%5Dh%5Bn-k%5D%7D

Convolution Integral

y(t)%20%3D%20%5Cint_%7B-%5Cinfty%7D%5E%7B%5Cinfty%7D%20x(%5Ctau)h(t-%5Ctau)%20d%5Ctau%20%5Ctriangleq%20x(t)%20*%20h(t)

卷积的求解法:

  • 常用:图解法(画出x和h,确定重合区域,最后计算即可)

  • 利用性质计算

3 求解微分方程(Differential Equation)

高数中教的是通解+特解的解法,这里不再赘述,而从物理意义上,我们更多使用零输入响应和零状态响应来理解

  • 零输入响应(zero-input response):理解成x(t) = 0

  • 零状态响应(zero-state response):理解成y(0) = y'(0) =  y''(0) = ... = 0

具体求解可以书上或者作业题找一道练练手,反正就是解两个微分方程呗,靠高数了

4 傅里叶级数(Fourier Series)

对于满足Dirichlet条件的周期函数,傅里叶级数可以把一些类波函数表示成一些三角函数相加

连续时间域上的傅里叶级数(CTFS)

x(t)%20%3D%20%5Csum_%7Bk%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%20%7Ba_k%20e%5E%7Bjk%5Comega_%7B0%7Dt%7D%7D

a_0%20%3D%20%5Cfrac%7B1%7D%7BT%7D%20%5Cint_%7BT%7Dx(t)%20dt

a_k%20%3D%20%5Cfrac%7B1%7D%7BT%7D%20%5Cint_%7BT%7Dx(t)e%5E%7B-jk%5Comega_%7B0%7Dt%7D%20dt

CTFS性质

  • 线性性:x(t)%20%5Cleftrightarrow%20a_k%20%2C%20y(t)%20%5Cleftrightarrow%20b_k%20 ,则  Ax(t)%2BBy(t)%20%5Cleftrightarrow%20Aa_k%2BBb_k

  • Time Shifting:x(t)%20%5Cleftrightarrow%20a_k ,则 %20%20x(t)%20%5Cleftrightarrow%20a_k

  • Time Reversal:%20%20x(t)%20%5Cleftrightarrow%20a_k, 则 x(-t)%20%5Cleftrightarrow%20a_%20%7B-k%7D

  • 共轭:x(t)%20%5Cleftrightarrow%20a_k , 则  x%5E%7B*%7D(t)%20%5Cleftrightarrow%20a_%7B-k%7D%5E%7B*%7D%20

  • 微分性: x(t)%20%5Cleftrightarrow%20a_k , 则  %5Cfrac%7Bdx(t)%7D%7Bdt%7D%20%5Cleftrightarrow%20(jk%5Comega_0)a_k 

  • 积分性:  x(t)%20%5Cleftrightarrow%20a_k  , 则  %5Cint_%7B-%5Cinfty%7D%5E%7Bt%7Dx(t)dt%20%5Cleftrightarrow%20%5Cfrac%7B1%7D%7Bjk%5Comega_0%7Da_k%20%20

  • 相乘: x(t)%20%5Cleftrightarrow%20a_k%20%2C%20y(t)%20%5Cleftrightarrow%20b_k%20,则 %20x(t)y(t)%20%5Cleftrightarrow%20a_k*b_k

Parseval Relation(等式的两边均代表平均功率)

%5Cfrac%7B1%7D%7BT%7D%5Cint_%7BT%7D%7Cx(t)%7C%5E%7B2%7Ddt%20%3D%20%5Csum_%7Bk%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%20%7B%7Ca_k%7C%5E%7B2%7D%7D


离散时间域上的傅里叶级数(DTFS)

x%5Bn%5D%20%3D%20%5Csum_%7Bk%20%3D%20%3CN%3E%7D%5E%7B%7D%20%7Ba_k%20e%5E%7Bjk(2%5Cpi%20%2FN)n%7D%7D

a_k%20%3D%5Cfrac%7B1%7D%7BN%7D%20%5Csum_%7Bn%20%3D%20%3CN%3E%7D%5E%7B%7D%20%7Bx%5Bn%5D%20e%5E%7B-jk%5Comega_0%20n%7D%7D

常见求FS的方法,

  • 定义法:利用CTFS和DTFS的系数定义来求

  • 运用展开式,将三角函数展开为指数形式,常用公式 cos%5Comega%20n%20%3D%20%5Cfrac%7B1%7D%7B2%7D(e%5E%7Bj%5Comega%20n%7D%20%2B%20e%5E%7B-j%5Comega%20n%7D) , sin%5Comega%20n%20%3D%20%5Cfrac%7B1%7D%7B2j%7D(e%5E%7Bj%5Comega%20n%7D%20-%20e%5E%7B-j%5Comega%20n%7D) ,然后比较系数即可

5 傅里叶变换

连续时间傅里叶变换(CTFT)

X(j%5Comega)%20%3D%20%5Cint_%7B-%5Cinfty%7D%5E%7B%2B%5Cinfty%7D%20x(t)e%5E%7B-j%5Comega%20t%7Ddt

x(t)%20%3D%20%5Cfrac%7B1%7D%7B2%5Cpi%7D%5Cint_%7B-%5Cinfty%7D%5E%7B%2B%5Cinfty%7D%20X(j%5Comega)e%5E%7Bj%5Comega%20t%7Dd%5Comega

然后这里主要涉及到的题型是傅里叶变换的求解,同时从这几年期末考试的情况来看,基本的傅里叶变换、拉普拉斯变换和z变换是会在appendix里面给出的,所以不需要去背常用的一些FT/LT/ZT,我们需要重点关注的是性质,然后灵活应用性质即可。对于性质,个人建议的学习方法是考前全部自己手推一遍,然后现在复习的时候就尽可能不要翻书了,把性质要牢记于心。上学期考数理方法的时候就感觉这个复习方法很好用,性质很快就熟记了

CTFT性质

  • 线性性:x(t)%20%5Cleftrightarrow%20X(j%5Comega)%20%2C%20y(t)%20%5Cleftrightarrow%20Y(j%5Comega)%20,则 %20Ax(t)%2BBy(t)%20%5Cleftrightarrow%20A%20X(j%5Comega)%2BBY(j%5Comega)

  • Time Shifting: x(t)%20%5Cleftrightarrow%20X(j%5Comega) 

  • Frequency Shifting: x(t)%20%5Cleftrightarrow%20X(j%5Comega), 则  x(t)e%5E%7B%5Cpm%20j%5Comega_0%20t%7D%20%5Cleftrightarrow%20X%5Bj(%5Comega%20%5Cpm%20%5Comega_0)%5D%20

  • Time/Frequency Scaling:  x(t)%20%5Cleftrightarrow%20X(j%5Comega), 则  x(at)%20%5Cleftrightarrow%20%5Cfrac%7B1%7D%7B%7Ca%7C%7DX(%5Cfrac%7Bj%5Comega%7D%7Ba%7D)

  • 共轭:  x(t)%20%5Cleftrightarrow%20X(j%5Comega) , 则  x%5E%7B*%7D(t)%20%5Cleftrightarrow%20X%5E%7B*%7D(-j%5Comega)%20

  • 微分性: x(t)%20%5Cleftrightarrow%20X(j%5Comega), 则  %20%5Cfrac%7Bdx(t)%7D%7Bdt%7D%20%5Cleftrightarrow%20j%5Comega%20X(j%5Comega)%20

  • 积分性: x(t)%20%5Cleftrightarrow%20X(j%5Comega) , 则  %5Cint_%7B-%5Cinfty%7D%5E%7Bt%7Dx(t)dt%20%5Cleftrightarrow%20%5Cfrac%7B1%7D%7Bj%5Comega%7DX(j%5Comega)%20%2B%20%5Cpi%20X(0)%5Cdelta%20(%5Comega)%20

  • 对偶性: X(j%5Comega)%20%3D%20%5Cint_%7B-%5Cinfty%7D%5E%7B%5Cinfty%7Dx(t)e%5E%7B-j%5Comega%20t%7Ddt%2C%20x(t)%20%3D%20%5Cfrac%7B1%7D%7B2%5Cpi%7D%20%5Cint_%7B-%5Cinfty%7D%5E%7B%5Cinfty%7DX(j%5Comega)e%5E%7Bj%5Comega%20t%7Dd%5Comega

  • 相乘: y(t)%20%3D%20h(t)x(t)%20,则  Y(j%5Comega)%20%3D%20%5Cfrac%7B1%7D%7B2%5Cpi%7D%20H(j%5Comega)*%20X(j%5Comega)

  • Parseval Relation

    %5Cfrac%7B1%7D%7B2%5Cpi%7D%5Cint_%7B-%5Cinfty%7D%5E%7B%5Cinfty%7D%7Cx(j%5Comega)%7C%5E%7B2%7Dd%5Comega%20%3D%20%5Cint_%7B-%5Cinfty%7D%5E%7B%5Cinfty%7D%20%7B%7Cx(t)%7C%5E%7B2%7D%20dt%7D


离散时间傅里叶变换(DTFT)

X(e%5E%7Bj%5Comega%7D)%20%3D%20%5Csum_%7Bn%20%3D%20-%5Cinfty%7D%5E%7B%2B%5Cinfty%7D%20x%5Bn%5De%5E%7B-j%5Comega%20n%7D

x%5Bn%5D%20%3D%20%5Cfrac%7B1%7D%7B2%5Cpi%7D%5Cint_%7B2%5Cpi%7D%20X(e%5E%7Bj%5Comega%7D)e%5E%7Bj%5Comega%20n%7Dd%5Comega

DTFT性质

  • 线性性: x%5Bn%5D%20%5Cleftrightarrow%20X(e%5E%7Bj%5Comega%7D)%20%2C%20y%5Bn%5D%20%5Cleftrightarrow%20Y(e%5E%7Bj%5Comega%7D)%EF%BC%8C%E5%88%99Ax%5Bn%5D%2BBy%5Bn%5D%20%5Cleftrightarrow%20A%20X(e%5E%7Bj%5Comega%7D)%2BBY(e%5E%7Bj%5Comega%7D)

  • Time Shifting:  x%5Bn%5D%20%5Cleftrightarrow%20X(e%5E%7Bj%5Comega%7D)%20%EF%BC%8C%E5%88%99x%5Bn-n_0%5D%20%5Cleftrightarrow%20X(e%5E%7Bj%5Comega%7D)%20e%5E%7B-j%5Comega%20n_0%7D ,

  • Frequency Shifting:x%5Bn%5D%20%5Cleftrightarrow%20X(e%5E%7Bj%5Comega%7D)%20%EF%BC%8C%E5%88%99x%5Bn%5De%5E%7B%5Cpm%20j%5Comega_0%20n%7D%20%5Cleftrightarrow%20X%5Be%5E%7Bj(%5Comega%20%5Cpm%20%5Comega_0)%7D%5D%20

  • 共轭: x%5Bn%5D%20%5Cleftrightarrow%20X(e%5E%7Bj%5Comega%7D)%20%EF%BC%8C%E5%88%99%20x%5E%7B*%7D%5Bn%5D%20%5Cleftrightarrow%20X%5E%7B*%7D(e%5E%7B-j%5Comega%7D)%20  

  • 微分性: x%5Bn%5D%20%5Cleftrightarrow%20X(e%5E%7Bj%5Comega%7D)%20 , 则  x%5Bn%5D-x%5Bn-1%5D%20%5Cleftrightarrow%20(1-e%5E%7B-j%5Comega%7D)X(e%5E%7Bj%5Comega%7D)%20

  • 积分性: x%5Bn%5D%20%5Cleftrightarrow%20X(e%5E%7Bj%5Comega%7D)%20 , 则  %5Csum_%7B-%5Cinfty%7D%5E%7Bn%7Dx%5Bn%5D%20%5Cleftrightarrow%20%5Cfrac%7B1%7D%7B1-e%5E%7B-j%5Comega%7D%7DX(e%5E%7Bj%5Comega%7D)%20%2B%20%5Cpi%20X(1)%5Csum_%7Bl%3D-%5Cinfty%7D%5E%7Bn%7D%20%5Cdelta%5B%5Comega%20-%202%5Cpi%20l%5D%20

  • 相乘: y%5Bn%5D%20%3D%20h%5Bn%5Dx%5Bn%5D%EF%BC%8C%E5%88%99Y(j%5Comega)%20%3D%20%5Cfrac%7B1%7D%7B2%5Cpi%7D%20H(e%5E%7Bj%5Comega%7D)*%20X(e%5E%7Bj%5Comega%7D)

  • 卷积: y%5Bn%5D%20%3D%20h%5Bn%5D%20*%20x%5Bn%5D%20%EF%BC%8C%E5%88%99Y(e%5E%7Bj%5Comega%7D)%20%3D%20H(e%5E%7Bj%5Comega%7D)%20X(e%5E%7Bj%5Comega%7D)

  • Parseval Relation

    %5Csum_%7Bn%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%7Cx%5Bn%5D%7C%5E%7B2%7D%20%3D%5Cfrac%7B1%7D%7B2%5Cpi%7D%20%5Cint_%7B2%5Cpi%7D%5E%7B%7D%20%7B%7Cx(e%5E%7Bj%5Comega%7D)%7C%5E%7B2%7D%20d%5Comega%7D


System Analysis

主要利用的性质

  • 微分性: x(t)%20%5Cleftrightarrow%20X(j%5Comega)%20%EF%BC%8C%E5%88%99%20%5Cfrac%7Bdx(t)%7D%7Bdt%7D%20%5Cleftrightarrow%20j%5Comega%20X(j%5Comega)%20

  • 积分性:x(t)%20%5Cleftrightarrow%20X(j%5Comega)%20%EF%BC%8C%E5%88%99%5Cint_%7B-%5Cinfty%7D%5E%7Bt%7Dx(t)dt%20%5Cleftrightarrow%20%20%5Cfrac%7B1%7D%7Bj%5Comega%7DX(j%5Comega)%20%2B%20%5Cpi%20X(0)%5Cdelta%20(%5Comega)%20

E.G

%5Cfrac%7Bd%5E%7B2%7Dy(t)%7D%7Bdt%5E%7B2%7D%7D%20%2B%204%5Cfrac%7Bdy(t)%7D%7Bdt%7D%20%2B%203y(t)%20%3D%20%5Cfrac%7Bdx(t)%7D%7Bdt%7D%20%2B%202x(t)

解答:利用微分性可得

H(j%5Comega)%20%3D%20%5Cfrac%7BY(j%5Comega)%7D%7BX(j%5Comega)%7D%20%3D%20%5Cfrac%7Bj%5Comega%20%2B%202%7D%7B(j%5Comega%20%2B%203)(j%5Comega%20%2B%201)%7D%20%3D%20%5Cfrac%7B1%2F2%7D%7Bj%5Comega%20%2B%203%7D%20%2B%20%5Cfrac%7B1%2F2%7D%7Bj%5Comega%20%2B%201%7D

h(t)%20%3D%200.5%20e%5E%7B-3t%7Du(t)%20%2B%200.5%20e%5E%7B-t%7Du(t)%20 (利用基本的Pair即可)


调制与解调(Modulation & Demodulation)

可以看一个例子来理解,这一块基本考试就是这一种方式了,会考察调制的结果,然后设计一个解调函数。

调制结果的计算比较简单,只需要利用公式

r(t)%20%3D%20s(t)p(t)%20%5Cleftrightarrow%20%20R(j%5Comega)%20%3D%20%5Cfrac%7B1%7D%7B2%5Cpi%7D%5BS(j%5Comega)*%20P(j%5Comega)%5D

解调的话一般来说还是需要利用函数平移,然后利用一个低通滤波器即可。这一块要求不高,简单理解即可。

采样(Sampling)

采样定理就是需要保证采样值可以包含原始信号的所有信息,被采样的信号可以不失真地还原成原始信号。

要求: f_s%20%3E%202f_%7Bmax%7D


6 拉普拉斯变换(Laplace Transform)

X(s)%20%3D%20%5Cint_%7B-%5Cinfty%7D%5E%7B%2B%5Cinfty%7D%20x(t)e%5E%7B-s%20t%7Ddt

Laplace Tranform和Fourier Transform之间重要的区别在于LT有ROC限制,满足条件的ROC应该是使得%20%5Cint_%7B-%5Cinfty%7D%5E%7B%5Cinfty%7Dx(t)e%5E%7B-%5Csigma%20t%7Ddt%20%3C%20%5Cinfty%0A 成立的 Re{s}

关于ROC的pole图,大家可以参考一下其他资料(受篇幅限制),理解起来不难,有些题可能需要你画出来pole。

求Laplace反变换, E.G.

X(s)%20%3D%20%5Cfrac%7Bs%2B3%7D%7B(s%2B1)(s-2)%7D

求解方法,先拆分成常见形式(系数可以用留数定理,很快可以算出来),最后别忘了对ROC分类讨论

X(s)%20%3D%20%5Cfrac%7B-2%2F3%7D%7Bs%2B1%7D%20%2B%20%5Cfrac%7B5%2F3%7D%7Bs-2%7D

(1)%20x(t)%20%3D%20-(%5Cfrac%7B-2%7D%7B3%7De%5E%7B-t%7D%20%2B%20%5Cfrac%7B5%7D%7B3%7De%5E%7B2t%7D)u(-t)%20%5Cquad%20ROC%3A%20Re%5C%7Bs%5C%7D%20%3C%20-1

(2)%20x(t)%20%3D%20-%5Cfrac%7B2%7D%7B3%7De%5E%7B-t%7Du(t)%20-%20%5Cfrac%7B5%7D%7B3%7De%5E%7B2t%7Du(-t)%20%5Cquad%20ROC%3A%20-1%3C%20Re%5C%7Bs%5C%7D%20%3C%202

(3)%20x(t)%20%3D%20-%5Cfrac%7B2%7D%7B3%7De%5E%7B-t%7Du(t)%20%2B%20%5Cfrac%7B5%7D%7B3%7De%5E%7B2t%7Du(t)%20%5Cquad%20ROC%3A%20%20Re%5C%7Bs%5C%7D%20%3E%202

其他一些常见形式,

  • %5Cfrac%7B1%7D%7Bs%5E%7B3%7D(s-1)%7D%20%3D%20%5Cfrac%7BK_1%7D%7Bs%7D%20%2B%20%5Cfrac%7BK_2%7D%7Bs%5E%7B2%7D%7D%20%2B%20%5Cfrac%7BK_3%7D%7Bs%5E%7B3%7D%7D%20%2B%20%5Cfrac%7BK_4%7D%7Bs-1%7D%20, 待定系数同样用留数定理可快速求解

  • %20%5Cfrac%7BMs%2BN%7D%7B(s-%5Calpha)%5E%7B2%7D%2B%5Cbeta%5E%7B2%7D%7D%20%3D%20%5Cfrac%7BK_1%7D%7Bs-%5Calpha%20-%20j%5Cbeta%7D%20%2B%20%5Cfrac%7BK_2%7D%7Bs-%5Calpha%20%2Bj%5Cbeta%7D ,一般可以写成三角函数的形式

LT性质与FT类似,在此就不占用空间啦(主要b站有公式上限orz)

初值/终值定理

x(0%5E%7B%2B%7D)%20%3D%20%5Clim_%7Bs%20%5Crightarrow%20%5Cinfty%7DsX(s)

lim_%7Bt%20%5Crightarrow%20%5Cinfty%7D%20x(t)%3D%20lim_%7Bs%20%5Crightarrow%200%7DsX(s)


system是否casual/stable的判断

  • casual system:最右端极点在右半平面

  • stable system:jw轴包含在ROC内即可

Steady-state Response

输入为

x(t)%20%3D%20Kcos(%5Comega_0%20t%20%2B%5Ctheta)u(t)

H(j%5Comega)%3D%7CH(j%5Comega)%7Ce%5E%7Bj%5Cphi(%5Comega)%7D

输出为

y(t)%3D%7CH(j%5Comega_0)%7CKcos%5B%5Comega_0%20t%20%2B%20%5Ctheta%20%2B%20%5Cphi(%5Comega_0)%5D

然后相关的系统框图需要有一定了解,个人认为系统框图可以考场现推,没有太大的必要死记硬背,就先把x的一阶二阶导设出来,然后化简式子连接即可。

然后还有一类题是用LT解微分方程,有初始值时,需要注意以下即可

%5Cfrac%7Bdx(t)%7D%7Bdt%7D%20%5Cleftrightarrow%20sX(s)%20-%20x(0%5E%7B-%7D)

%5Cfrac%7Bd%5E%7B2%7Dx(t)%7D%7Bdt%5E%7B2%7D%7D%20%5Cleftrightarrow%20s%5E%7B2%7DX(s)%20-%20sx(0%5E%7B-%7D)%20-%20x%5E%7B'%7D(0%5E%7B-%7D)


7 Z变换(Z Transform)

x%5Bn%5D%20%5Cleftrightarrow%20X(z)%20%3D%20%5Csum_%7Bn%20%3D%20-%5Cinfty%7D%5E%7B%5Cinfty%7D%20x%5Bn%5Dz%5E%7B-n%7D

最常见的z变换pair基本就是 a%5E%7Bn%7Du(n)%20%5Cleftrightarrow%20%5Cfrac%7B1%7D%7B1-az%5E%7B-1%7D%7D然后针对ROC,x[n]若为右单边, %7Cz%7C%3Er_0 ; x[n]若为左单边, %7Cz%7C%3Cr_0%20 ; x[n]若为双边, 是一个圆环

%5Cfrac%7Bz%7D%7Bz-a%7D%20%20%5Cleftrightarrow%20%5Cbegin%7Bcases%7D%20a%5E%7Bn%7Du(n)%20%5Cquad%20%7Cz%7C%20%3E%20%7Ca%7C%20%5C%5C%20-a%5E%7Bn%7Du(-n-1)%20%5Cquad%20%7Cz%7C%20%3C%20%7Ca%7C%20%5Cend%7Bcases%7D


反z变换的求法

  • 先做%20%5Cfrac%7BX(z)%7D%7Bz%7D , 然后利用留数定理拆分成以下形式%5Cfrac%7BA_0%7D%7Bz%7D%20%2B%20%5Cfrac%7BA_1%7D%7Bz-z_%7B1%7D%7D%20%2B%20%5Cfrac%7BA_2%7D%7Bz-z_2%7D%2B...%2B%5Cfrac%7BA_N%7D%7Bz-z_N%7D

  • 得到反变换形式,x(n)%20%3D%20%5Csum_%7Bi%20%3D%201%7D%5E%7BN%7DA_i%20(z_i)%5E%7Bn%7Du(n)

ZT性质与FT类似,在此就不占用空间啦(主要b站有公式上限orz)

初值定理与终值定理

x%5B0%5D%20%3D%20%5Clim_%7Bz%20%5Crightarrow%20%5Cinfty%7D%20X(z)

%5Clim_%7Bn%20%5Crightarrow%20%5Cinfty%7D%20x%5Bn%5D%20%3D%20%5Clim_%7Bz%20%5Crightarrow%201%7D%20(z-1)X(z)


system是否casual/stable的判断

  • LTI stable: ROC包含单位圆

  • LTI casual: 在无穷远处收敛,即H(z)分母阶数比分子高,ROC包含z平面上的无穷

系统框图个人感觉和Laplace差不多,其实这仨掌握一个其他都是同理的。

解微分方程:

  • x%5Bn%5D%20%5Cleftrightarrow%20X(z)

  • x%5Bn-1%5D%20%5Cleftrightarrow%20z%5E%7B-1%7DX(z)%20%2Bx%5B-1%5D

  • x%5Bn-2%5D%20%5Cleftrightarrow%20z%5E%7B-2%7DX(z)%20%2Bz%5E%7B-1%7Dx%5B-1%5D%2Bx%5B-2%5D

  • x%5Bn%2B1%5D%20%5Cleftrightarrow%20zX(z)%20-zx%5B0%5D

  • x%5Bn%2B2%5D%20%5Cleftrightarrow%20z%5E%7B2%7DX(z)%20-z%5E%7B2%7Dx%5B0%5D-zx%5B1%5D

欢迎各位大佬提出建议啦~b站只允许插100个公式+图片,所以很多内容被迫删除,选取了最重要+最基础的一些

SJTU Signal&System 期末整理的评论 (共 条)

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