代码搜索：Problem
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www.eeworm.com/read/143706/12849984
m demmlp2.m
%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron
%
% Description
% The problem consists of input data in two dimensions drawn from a
% mixture of three Gaussians: two of which
www.eeworm.com/read/143592/12858932
cpp prg15_5.cpp
// File: prg15_5.cpp
// the program demonstrates the dynamic programming solution
// to the knapsack problem. the vector itemList contains
// five items, each with a specified size and value. after
www.eeworm.com/read/244377/12869965
bak lpvsolpq.bak
% [gam,xmat,ymat,xyopt] = ...
% lpvsolpq(vlpv,dim,vnu,fghparm,gradfg,gamwt,slow,xyinit)
% calculates the solution to the parameter-dependent OSF LPV control problem
% with dynamic parameter measureme
www.eeworm.com/read/244377/12870005
m lpvsolpq.m
% [gam,xmat,ymat,xyopt] = ...
% lpvsolpq(vlpv,dim,vnu,fghparm,gradfg,gamwt,slow,xyinit)
% calculates the solution to the parameter-dependent OSF LPV control problem
% with dynamic parameter measureme
www.eeworm.com/read/329752/12935680
c psh2.c
/** prompting shell version 2
**
** Solves the `one-shot' problem of version 1
** Uses execvp(), but fork()s first so that the
** shell waits around to perform another command
** New probl
www.eeworm.com/read/327948/13054077
m pr3_25_ss_counternarrowbandjammer_noband-eliminationfiltering.m
%Problem3.25
%Simulates antijamming features of spread spectrum signal (no band elimination used) against narrow
%band jammer and compares to the ones of plain signals;
clear all; close all;
t=[
www.eeworm.com/read/327948/13054088
m pr3_26_ss_counternarrowbandjammer_withband-eliminationfiltering.m
%Problem 3.26;
%Simulates antijamming features of spread spectrum signal (band elimination used) against narrow
%band jammer and compares to the ones of plain signals;
clear all; close all;
t=[0
www.eeworm.com/read/140851/13058940
m demhmc3.m
%DEMHMC3 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by samplin
www.eeworm.com/read/140851/13058955
m demolgd1.m
%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampli
www.eeworm.com/read/140851/13058994
m demkmn1.m
%DEMKMEAN Demonstrate simple clustering model trained with K-means.
%
% Description
% The problem consists of data in a two-dimensional space. The data is
% drawn from three spherical Gaussian di