代码搜索：Simulated

找到约 1,823 项符合「Simulated」的源代码

代码结果 1,823
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www.eeworm.com/read/415311/11077248

m deterministic_sa.m

function [features, targets] = Deterministic_SA(train_features, train_targets, params, region, plot_on) %Reduce the number of data points using the deterministic simulated annealing algorithm %Inp
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www.eeworm.com/read/411970/11218917

txt readme.txt

simcdma.m allows a set of CDMA parameters to be simulated, such as BER verse number of users, etc. All the graphs used in the thesis were generated using this script. The output from this script is
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www.eeworm.com/read/334935/12561081

m fxquant.m

function X = fxquant( s, bit, rmode, lmode ) %FXQUANT simulated fixed-point arithmetic %------- % Usage: X = fxquant( S, BIT, RMODE, LMODE ) % % returns the input signal S reduced to a w
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www.eeworm.com/read/135217/13949734

txt readme.txt

simcdma.m allows a set of CDMA parameters to be simulated, such as BER verse number of users, etc. All the graphs used in the thesis were generated using this script. The output from this script is
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www.eeworm.com/read/391573/8396611

as particle3d_class.as

/* Particle3d Class Oct. 29, 2002 (c) 2002 Robert Penner This custom object represents and renders a visual particle existing in a simulated three-dimensional space. The appeara
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www.eeworm.com/read/286662/8751761

m stochastic_sa.m

function [patterns, targets] = Stochastic_SA(train_patterns, train_targets, params, plot_on) %Reduce the number of data points using the stochastic simulated annealing algorithm %Inputs: % train_
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www.eeworm.com/read/429621/8798308

m m1o.m

% M file for Project 1 on induction motor drive % with open loop control in Chapter 9 % It sets the machine parameters and also plots the simulated % results when used in conjunction with s1open.m
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www.eeworm.com/read/372113/9521151

m stochastic_sa.m

function [patterns, targets] = Stochastic_SA(train_patterns, train_targets, params, plot_on) %Reduce the number of data points using the stochastic simulated annealing algorithm %Inputs: % train_
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www.eeworm.com/read/362008/10023846

m stochastic_sa.m

function [patterns, targets] = Stochastic_SA(train_patterns, train_targets, params, plot_on) %Reduce the number of data points using the stochastic simulated annealing algorithm %Inputs: % train_
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www.eeworm.com/read/357874/10199091

m stochastic_sa.m

function [patterns, targets] = Stochastic_SA(train_patterns, train_targets, params, plot_on) %Reduce the number of data points using the stochastic simulated annealing algorithm %Inputs: % train_
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