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m tlms2.m

%TLMS2 Problem 1.1.1.2.2 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % a1 - coefficient of input AR process % sigmax - standard de
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m rls3.m

%RLS3 Problem 1.1.1.2.3 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % a1 - coefficient of input AR process % sigmax - standard dev
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m nlrls2.m

%NLRLS2 Problem 1.1.1.2.5 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % a1 - coefficient of input AR process % sigmax - standard d
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m lms5.m

%LMS5 Problem 2.1 % % 'ifile.mat' - input file containing: % K - iterations % H - FIR channel % Neq - equalizer order % sigman - standard deviation of noise at channel ou
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m sfrls2.m

%SFRLS2 Problem 1.1.1.2.7 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % a1 - coefficient of input AR process % sigmax - standard d
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h travel.h

/* Copyright (c) 2001 Author: Konstantin Boukreev E-mail: konstantin@mail.primorye.ru Created: 07.09.2001 17:14:06 Version: 1.0.0 Genome for Travelling Salesman Problem */
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m tesfunc.m

function [sys,x0,str,ts] = tesfunc(t,x,u,flag) % Tennessee Eastman Process Control Test Problem % Re-Written in MATLAB 5.2 % by % Martin Br
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m demhmc1.m

%DEMHMC1 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians. % % Description % The problem consists of generating data from a mixture of two % Gaussians in two dimensions using a hybr
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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 distri
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m demknn1.m

%DEMKNN1 Demonstrate nearest neighbour classifier. % % Description % The problem consists of data in a two-dimensional space. The data is % drawn from three spherical Gaussian distributions with prio
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