代码搜索：Matrix
找到约 10,000 项符合「Matrix」的源代码
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www.eeworm.com/read/386807/8724423
m kalmanfilter.m
%用老方法滤波50次;stardard kalman filtering
for m=1:num
PHI= [1,T,0,0,0,0; %PHI is the state transition matrix
0,1,0,0,0,0;
0,0,1,T,0,0;
0,0,0,1,0,0;
www.eeworm.com/read/430775/8728866
m losstest.m
% MATLAB function losstest.m checks out whether
% or not a Q-port network is lossless. For that
% purpose, the network's Q-by-Q scattering matrix, S,
% must be specified.
% Invocation of function
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m losstest.m
% MATLAB function losstest.m checks out whether
% or not a Q-port network is lossless. For that
% purpose, the network's Q-by-Q scattering matrix, S,
% must be specified.
% Invocation of function
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m adjcentmatrix - 复制 - 复制.m
clear;
p=0.5; %the probability of the bonds
N=50; %the number of nodes is N^2
%%%%%%%%%%%%%create the adjacency matrix of a percolation network
B=zeros(N^2);
for i=1:2:(2*N-1)
f
www.eeworm.com/read/430725/8730581
m adjcentmatrix - 复制.m
clear;
p=0.5; %the probability of the bonds
N=50; %the number of nodes is N^2
%%%%%%%%%%%%%create the adjacency matrix of a percolation network
B=zeros(N^2);
for i=1:2:(2*N-1)
f
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m adjcentmatrix.m
clear;
p=0.5; %the probability of the bonds
N=50; %the number of nodes is N^2
%%%%%%%%%%%%%create the adjacency matrix of a percolation network
B=zeros(N^2);
for i=1:2:(2*N-1)
f
www.eeworm.com/read/430527/8740766
m nlgen.m
function y = nlgen (x, h, q)
%NLGEN generates the output of a second order Volterra system
% y = nlgen (x, h, q)
% y(n) = sum_{k} h(k) x(n-k) + sum_{k} sum_{l} q(k,l) x(n-k)x(n-l)
% the
www.eeworm.com/read/286669/8750923
txt pcac++.txt
C*********************** Contents ****************************************
C* Principal Components Analysis: C, 638 lines. ****************************
C* Sample input data set (final 36 lines). *
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m sima1.m
function [nw,a1,i] = sima1(w,p,lr,rho,pf)
%SIMA1 ART1 simulation function.
% Each input vector is presented to the network one at a time.
% (See COMPET, HARDLIM)
%
% [NW,A1,
www.eeworm.com/read/286440/8763846
m l2_distance.m
function d = L2_distance(a,b,df)
% L2_DISTANCE - computes Euclidean distance matrix
%
% E = L2_distance(A,B)
%
% A - (DxM) matrix
% B - (DxN) matrix
% df = 1, force diagonals to be zero; 0 (