代码搜索:Generalized

找到约 2,645 项符合「Generalized」的源代码

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txt readme.txt

@ DPSO is based on the PSO version by Yuhui SHI. @ Please see the OLD_README before reading this file!!! /==================== Other Information =============================/ Dissipative PSO
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m ex5_2.m

% Example 5-2: Computation of non-square % time-varying channel transfer matrix Wc clear all K = 4; % Number of time samples (I/O observations) N = 2; % Number of channel inputs M = 3; % Number
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m ex5_1.m

% Example 5-1: Computation of Time-varying Channel % Transfer Matrix Wc clear all K = 4; % Number of time samples (I/O observations) N = 4; % Number of channel inputs M = 4; % Number of channel
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txt readme.txt

********************************************************************************* * Generalized Disciminant Analysis for MataLab version for Windows 5.2.0.3084 * * * * Gaston Baudat & Fat
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m contents.m

% Generalized Anderson's task. % % andrdemo - Demonstrates algorithms which find solution of % the Generalized Anderson's task (GAT). % % Algorithms: % oanders - Original Ander
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in glm.in

GLM: Data file for testing Generalized Linear Regression Model routines 6 Number of values of M, P, and N 0 5 8 15 20 40 Values of M (row dimension)
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m glminit.m

function net = glminit(net, prior) %GLMINIT Initialise the weights in a generalized linear model. % % Description % % NET = GLMINIT(NET, PRIOR) takes a generalized linear model NET and % sets the weig
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m demglm2.m

%DEMGLM2 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da
www.eeworm.com/read/415313/11076555

m glminit.m

function net = glminit(net, prior) %GLMINIT Initialise the weights in a generalized linear model. % % Description % % NET = GLMINIT(NET, PRIOR) takes a generalized linear model NET and % sets the weig
www.eeworm.com/read/415313/11076727

m demglm2.m

%DEMGLM2 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da