代码搜索：Generalized
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www.eeworm.com/read/318141/13485108
htm ii36generalizedstochasticpetrinets.htm
II.3.6. Generalized Stochastic Petri Nets
www.eeworm.com/read/312163/13617414
m pandr.m
function varargout = pandr(model,distrib)
% PANDR Visualizes solution of the Generalized Anderson's task.
%
% Synopsis:
% h = pandr(model)
%
% Description:
% It vizualizes solution of the Gen
www.eeworm.com/read/312163/13617625
m contents.m
% Optimization methods for STPRtoolbox.
%
% gmnp - Solves Generalized Minimal Norm (GMNP) problem.
% gnnls - Solves Generalized Non-negative Least Squares (GNNLS) problem.
% gnpp - S
www.eeworm.com/read/312163/13617649
m~ contents.m~
% Optimization methods for STPRtoolbox.
%
% gmnp - Solves Generalized Minimal Norm (GMNP) problem.
% gnnls - Solves Generalized Non-negative Least Squares (GNNLS) problem.
% gnpp - S
www.eeworm.com/read/304357/13795582
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
www.eeworm.com/read/485544/6552736
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/485544/6552812
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/156528/11795260
m gld_inv.m
function x = gld_inv(cp,lambda)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% GLD_INV - Calculates the values of the inverse function of the cumulative
% distribu
www.eeworm.com/read/155109/11898424
m outer.m
%OUTER Outer product generalized
%
% function m = outer(v1,f,v2)
%
% v1,v2 row vectors
% m matrix, the elements are the res-
% ult of applying 'f' to the
% cartesian product of v1 and v2
%
www.eeworm.com/read/255595/12069762
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