代码搜索：plot
找到约 10,000 项符合「plot」的源代码
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www.eeworm.com/read/326135/13163155
m vistformfwd.m
function vistformfwd(tform, wdata, zdata, N)
%VISTFORMFWD Visualize forward geometric transform.
% VISTFORMFWD(TFORM, WRANGE, ZRANGE, N) shows two plots: an N-by-N
% grid in the W-Z coordinate
www.eeworm.com/read/326015/13170466
m bp_pso.m
%混合bp算法，即pso_bp算法：
%算法思路：利用pso的全局搜索能力对bp网络的权值进行优化，再利用bp算法对其进行局部寻优。
%pso算法部分如下：
%1.1 初始化格式
clear all
clc;
format long;
%1.2 初始化微粒群的参数
wmax=1.2;
wmin=0.6;
N = 40 ; %微粒数目
%N=60;
D = 16 ; %每个微
www.eeworm.com/read/326004/13171198
txt 一个梯度优化算法的实例(演示).txt
%计算任务：求 f(x,y)=2*x^2+2*x+y^2-y的最小值。
%凸函数优化。
%%%%%%% 函数图像 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
x0=-5:0.1:5;y0=x0;
[X,Y]=meshgrid(x0,y0);
fxy=2*X.^2+2*X+Y.^2-Y;
surfc(X,Y,fxy);
title('Su
www.eeworm.com/read/325882/13177539
m chap5_3.m
%Equivalent Discrete Sliding Mode Control based on RBF neural control
clear all;
close all;
ts=0.001;
a=25;
b=133;
A=[0,1;0,-a];
B=[0;b];
C=[1,0];
D=0;
[A1,B1,C1,D1]=c2dm(A,B,C,D,ts,'z');
www.eeworm.com/read/240954/13186310
m grnn.m
% 广义回归网络
%
clf;
figure(gcf);
setfsize(300,300);
echo on
clc
P=[1 2 3 4 5 6 7 8];
T=[0 1 2 3 2 1 2 1];
plot(P,T,'.','markersize',20);
axis([0 9 -1 4]);
pause
net=newgrnn(P,T,0.7);
A=si
www.eeworm.com/read/139096/13188824
m kalmanra2.m
%光纤陀螺零漂数据的卡尔曼滤波法
%利用AR模型参数的最小二乘估计计算陀螺采样数据的fai1,fai2及系统的过程噪声方差dsf
clear
clc
format long
b=dlmread('tt.txt');%读数据文件
ax=b(:,3)*1000;
avax=mean(ax)
n=size(ax);%取得数据长度
for i=1:n
ax(i)=ax(i)
www.eeworm.com/read/325628/13193286
m 3-4-5-2.m
%绘制指数函数曲线
p=-1:0.05:1;
t=exp(-p);
plot(p,t);
grid;
title('exponential function');
xlabel('x');
ylabel('y');
figure;
%建立并训练网络
for i=1:5
net=newgrnn(p,t,i/10);
y(i,:)=sim(net,p);
en
www.eeworm.com/read/139007/13195605
m ps.m
function y = ps(A, m, tol, rl, marksize)
%PS Dot plot of a pseudospectrum.
% PS(A, M, TOL, RL) plots an approximation to a pseudospectrum
% of the square matrix A, using M random pe
www.eeworm.com/read/139007/13195630
m gersh.m
function [G, e] = gersh(A, noplot)
%GERSH Gershgorin disks.
% GERSH(A) draws the Gershgorin disks for the square matrix A.
% The eigenvalues are plotted as crosses `x'.
%
www.eeworm.com/read/138978/13198607
m nlreg.m
%
% 2D Nonlinear Regression Fit of y = f(x)
%
% [ p, resid, h ] = nlreg( y, x, po, pr, 'func' )
%
% where p = fitted parameters
% resid = residuals
% h = handles