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www.eeworm.com/read/145776/12703082
m se2.m
%SE2 Problem 4.2
%
% 'ifile.mat' - input file containing:
% I - members of ensemble
% K - iterations
% s - deterministic part of reference signal
% sigman - standard devi
www.eeworm.com/read/145776/12703092
m nlms2.m
%NLMS2 Problem 4.4
%
% 'ifile.mat' - input file containing:
% I - members of ensemble
% K - iterations
% s - deterministic part of reference signal
% sigman - standard de
www.eeworm.com/read/145776/12703098
m eflrls2.m
%EFLRLS2 Problem 1.1.1.2.6
%
% 'ifile.mat' - input file containing:
% I - members of ensemble
% K - iterations
% a1 - coefficient of input AR process
% sigmax - standard
www.eeworm.com/read/145776/12703118
m pte2.m
%PTE2 Problem 4.3
%
% 'ifile.mat' - input file containing:
% I - members of ensemble
% K - iterations
% s - deterministic part of reference signal
% sigman - standard dev
www.eeworm.com/read/145776/12703124
m eflrls3.m
%EFLRLS3 Problem 2.2
%
% 'ifile.mat' - input file containing:
% K - iterations
% H - FIR channel
% Neq - equalizer order
% sigman - standard deviation of noise at channel
www.eeworm.com/read/145776/12703136
m lms6.m
%LMS6 Problem 4.1
%
% 'ifile.mat' - input file containing:
% I - members of ensemble
% K - iterations
% s - deterministic part of reference signal
% sigman - standard dev
www.eeworm.com/read/145776/12703155
m pte1.m
%PTE1 Problem 3.2
%
% 'ifile.mat' - input file containing:
% I - members of ensemble
% K - iterations
% s - deterministic part of signal to predict
% sigman - standard de
www.eeworm.com/read/246726/12706914
c dlp.c
/*
Author: Pate Williams (c) 1997
The following program implements and tests two
algorithms for solving the discrete logarithm
problem. The algorithms are baby-step giant-step
and Po
www.eeworm.com/read/143959/12827384
cs svm.cs
/*
* Conversion notes:
* Using JLCA 3.0, the only problem was with the save and loads. I changed them both to be
* StreamR/W around a FileStream. Originally, the save was a BinaryWriter over a F
www.eeworm.com/read/143706/12849489
m demev1.m
%DEMEV1 Demonstrate Bayesian regression for the MLP.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by compu