代码搜索：BATCH
找到约 6,125 项符合「BATCH」的源代码
代码结果 6,125
www.eeworm.com/read/259985/11753427
log batch_drc.log
(------------------------------------------------------------)
( )
( Batch DRC Update )
(
www.eeworm.com/read/131904/14120465
m batch1.m
make_rp;
rp.T = 2.9; rp.name = 'run11'; run_qrd_lsl(rp);
rp.T = 3.1; rp.name = 'run12'; run_qrd_lsl(rp);
rp.T = 3.3; rp.name = 'run13'; run_qrd_lsl(rp);
rp.T = 3.5; rp.name = 'run14'; run_qrd_lsl(rp)
www.eeworm.com/read/131904/14120473
m batch2.m
make_rp;
rp.T = 2.9; rp.name = 'run11'; run_qrd_lsl(rp);
rp.T = 3.1; rp.name = 'run12'; run_qrd_lsl(rp);
rp.T = 3.3; rp.name = 'run13'; run_qrd_lsl(rp);
rp.T = 3.5; rp.name = 'run14'; run_qrd_lsl(rp)
www.eeworm.com/read/131588/14136153
m backpropagation_batch.m
function [D, Wh, Wo] = Backpropagation_Batch(train_features, train_targets, params, region)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% features- Train
www.eeworm.com/read/131588/14136199
m perceptron_batch.m
function D = Perceptron_Batch(train_features, train_targets, params, region)
% Classify using the batch Perceptron algorithm
% Inputs:
% features - Train features
% targets - Train targets
www.eeworm.com/read/130592/14182280
m batch1.m
make_rp;
rp.T = 2.9; rp.name = 'run11'; run_qrd_lsl(rp);
rp.T = 3.1; rp.name = 'run12'; run_qrd_lsl(rp);
rp.T = 3.3; rp.name = 'run13'; run_qrd_lsl(rp);
rp.T = 3.5; rp.name = 'run14'; run_qrd_lsl(rp)
www.eeworm.com/read/130592/14182285
m batch2.m
make_rp;
rp.T = 2.9; rp.name = 'run11'; run_qrd_lsl(rp);
rp.T = 3.1; rp.name = 'run12'; run_qrd_lsl(rp);
rp.T = 3.3; rp.name = 'run13'; run_qrd_lsl(rp);
rp.T = 3.5; rp.name = 'run14'; run_qrd_lsl(rp)
www.eeworm.com/read/232339/14197238
m batch_plsgui.m
function batch_plsgui(varargin)
if nargin < 1
error('Usage: batch_plsgui(batch_text_file_name(s))');
end
for i = 1:nargin
batch_file = varargin{i};
fid = fopen(bat
www.eeworm.com/read/129915/14217599
m backpropagation_batch.m
function [D, Wh, Wo] = Backpropagation_Batch(train_features, train_targets, params, region)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% features- Train