求matlab离散化程序

clear all

close all

ts=0.001

J=1/147

q=22/147

sys=tf(1,[J,q,0])

dsys=c2d(sys,ts,'z')

[num,den]=tfdata(dsys,'v')

r_1=0r_2=0

u_1=0u_2=0

y_1=0y_2=0

error_1=0

ei=0

for k=1:1:1000

time(k)=k*ts

S=1

if S==1 %Step Signal

rin(k)=1.0

elseif S==2 %Sine Signal

rin(k)=1.0*sin(1*2*pi*k*ts)

end

yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1+num(3)*u_2

error(k)=rin(k)-yout(k)

derror(k)=(error(k)-error_1)/晌首ts

ap=22bp=8.0cp=0.8

kp(k)=ap+bp*(1-sech(cp*error(k)))

ad=0.5bd=2.5cd=6.5dd=0.30

kd(k)=ad+bd/(1+cd*exp(dd*error(k)))

ai=1ci=1

ki(k)=ai*sech(ci*error(k))

ei=ei+error(k)*ts

u(k)=kp(k)*error(k)+kd(k)*derror(k)+ki(k)*ei

%Update Parameters

r_2=r_1r_1=rin(k)

u_2=u_1u_1=u(k)

y_2=y_1y_1=yout(k)

error_1=error(k)

end

figure(1)

plot(time,rin,'k',time,yout,'k')

xlabel('time(s)')ylabel('rin,yout')

figure(2)

plot(time,rin-yout,'k')ylabel('error')

xlabel('time(s)')ylabel('宴森数error')

figure(3)

plot(time,derror,'k')

xlabel('time(s)')ylabel('derror')

M=1

if M==1

figure(4)

subplot(311)

plot(error,kp,'k')xlabel('error')ylabel('kp')

subplot(312)

plot(error,kd,'k')xlabel('error')ylabel('kd')

ad+bd/(1+cd)

subplot(313)

plot(error,ki,'k')xlabel('error')ylabel('ki')

elseif M==2

figure(5)

subplot(311)

plot(time,kp,'k')xlabel('time(s)')ylabel('kp')

subplot(312)

plot(time,kd,'k')xlabel('time(s)')ylabel('kd')

subplot(313)

plot(time,ki,'k')xlabel('春渣time(s)')ylabel('ki')

end

-----data_seperate.m-----------------------------------------------------------

%%数据初始化

data='D:\work\数据离散化代码\discretization_data.xls'

k=4

%%数据读入

[data,~]=xlsread(data)

rows=size(data,1)

%%等宽离散化，规则销码需要自己定义

rules=[0,0.179,0.258,0.35,0.504]

width_data=zeros(rows,2)

width_data(:,1)=data

width_data(:,2)=arrayfun(@find_type,data)

%%等频离散化

frequent_data=zeros(rows,2)

frequent_data(:,1)=data

end_=-1

for i=1:k-1

start_=floor((i-1)*rows/k)+1

end_=floor(i*rows/k)

frequent_data(start_:end_,2)=i

end

frequent_data(end_+1:end,2)=k

%%聚类离散化

[idx,~]=kmeans(data,k)

cluster_data=zeros(rows,2)

cluster_data(:,1)=data

cluster_data(:,2)=idx

figure

cust_subplot(width_data,3,1,1,k)

cust_subplot(frequent_data,3,1,2,k)

cust_subplot(cluster_data,3,1,3,k)

disp('数据离散化完成！')

-------------cust-subplot.m------------------------------------------------

function cust_subplot(width_data,rows,cols,index,k)

%% 自定义画图亏迅哪

subplot(rows,cols,index)

%%定义图形颜色和数据点的格式

dot_str ={'k*','ko','ks','kd'}

hold on

for i=1:k

data_ = width_data((width_data(:,2)==i),1)

num = size(data_,1)

y= zeros(num,1)

%%y(:)表示y数组（或向量）中的每一个昌困元素

y(:)=0.5*(i+1)

plot(data_,y,dot_str{1,i})

% plot(data_,y)

end

%title(title_)

hold off

end

——------------find_type.m---------------------------------------------------------------

function [ flag ] = find_type( data )

%% 根据规则，返回类别,1,2,3,4中的一个

rules = [0,0.179,0.258,0.35,0.504]

cols = size(rules,2)

for i=1:cols-1

if data>rules(i) &&data<=rules(i+1)

flag =i

return

end

end

flag =-1

end

可以运行出来的代码

首先，计算机处理的信号均为态悄册离散化的信号。

对于连续模拟信号的表达式，只要给定离散的时间参数，其得帆宏到的就是离散信号

例如运洞

sin(2*pi*100*t) 这表示频率为100Hz的正弦信号

那么，令t = 1:0.1:100

y = sin(2*pi*100*t)

得到的就是离散的正弦信号y.

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