代码搜索：Generates

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m bdt_f533.m

% MATLAB script that generates the probability of error versus the signal to noise ratio. initial_snr=0; final_snr=12; snr_step=0.75; tolerance=eps; % Tolerance used for the integration plus_
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m bdt_f531.m

% MATLAB script that generates the probability of error versus the signal to noise ratio. initial_snr=0; final_snr=12; snr_step=0.75; tolerance=eps; % Tolerance used for the integration plus_
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www.eeworm.com/read/412611/11190600

m altes.m

function x=altes(N,fmin,fmax,alpha) ; %ALTES Altes signal in time domain. % X=ALTES(N,FMIN,FMAX,ALPHA) generates the Altes signal in % the time domain. % % N : number of points in time %
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m fmodany.m

function [y,iflaw]=fmodany(iflaw,t0); %FMODANY Signal with arbitrary frequency modulation. % [Y,IFLAW]=FMODANY(IFLAW,T0) generates a frequency modulated % signal whose instantaneous frequency law is a
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m randfilt.m

function [y,zf,u]=randfilt(pb,pa,ny,zi) %RANDFILT Generate filtered gaussian noise without initial transient % % Generates noise by passing gaussian noise through a filter pb/pa % output signal y
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c alg49.c

#include #include #include /* generates: original element sequence of first container: 0 1 2 3 4 5 6 7 8 9 original element sequence of second cont
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m fmodany.m

function [y,iflaw]=fmodany(iflaw,t0); %FMODANY Signal with arbitrary frequency modulation. % [Y,IFLAW]=FMODANY(IFLAW,T0) generates a frequency modulated % signal whose instantaneous frequency law is a
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www.eeworm.com/read/334675/12580974

m spir.m

function S = spir; % % Ph.D. Thesis % Leandro Nunes de Castro % February, 2000 % Function generates the dataset for the SPIRAL problem % % SPIRAL 1 step = 0.1; t = 0:step:3*pi; l = lengt
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m dendrogram.m

function [h,T] = dendrogram(Z,p) %DENDROGRAM Generate dendragram plot. % DENDROGRAM(Z) generates a dendrogram from the output matrix of % LINKAGE. Z is a (M-1) by 3 matrix. M is the number of
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m donuts1.m

function Ag = donuts1(n); % % Ph.D. Thesis % Leandro Nunes de Castro % April, 2000 % Function generates the dataset for the 2-DONUTS problem % % n -> Number of training samples (odd numbers
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