this m file can Find a (near) optimal solution to the Traveling Salesman Problem (TSP) by setting up a Genetic Algorithm (GA) to search for the shortest path (Least distance needed to travel to each city exactly once)
Notes:
1. Input error checking included
2. Inputs can be specified in any order, so long as the parameter pairs are specified as a parameter , value
This unit uses an array of bytes to represent a LARGE number. The number is binairy-stored in the array, with the Least Significant Byte (LSB) first and the Most Significant Byte (MSB) last, like all Intel-integer types.
By building a nonlinear function relationship between an d the error signal，this paper presents a no—
vel variable step size LMS(Least Mean Square)adaptive filtering algorithm．
New users and old of optimization in MATLAB will find useful tips and tricks in this document, as well as examples one can use as templates for their own problems.
Use this tool by editing the file optimtips.m, then execute blocks of code in cell mode from the editor, or best, publish the file to HTML. Copy and paste also works of course.
Some readers may find this tool valuable if only for the function pleas - a partitioned Least squares solver based on lsqnonlin.
This is a work in progress, as I fully expect to add new topics as I think of them or as suggestions are made. Suggestions for topics I ve missed are welcome, as are corrections of my probable numerous errors. The topics currently covered are listed below
Finds a (near) optimal solution to the Traveling Salesman Problem (TSP) by setting up a Genetic Algorithm (GA) to search for the shortest path (Least distance needed to travel to each city exactly once)
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
Least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
Least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
Traveling Salesman Problem (TSP) has been an interesting problem for a long
time in classical optimization techniques which are based on linear and nonlinear
programming. TSP can be described as follows: Given a number of cities to visit
and their distances from all other cities know, an optimal travel route has to be
found so that each city is visited one and only once with the Least possible distance
traveled. This is a simple problem with handful of cities but becomes complicated
as the number increases.
PCA and PLS aims:to get some
insight into the bilinear factor models Principal Component Analysis
(PCA) and Partial Least Squares (PLS) regression, focusing on the
mathematics and numerical aspects rather than how s and why s of
data analysis practice. For the latter part it is assumed (but not
absolutely necessary) that the reader is already familiar with these
methods. It also assumes you have had some preliminary experience
with linear/matrix algebra.
μC/OS-II Goals
Probably the most important goal of μC/OS-II was to make it backward compatible with μC/OS (at Least from an
application’s standpoint). A μC/OS port might need to be modified to work with μC/OS-II but at Least, the application
code should require only minor changes (if any). Also, because μC/OS-II is based on the same core as μC/OS, it is just
as reliable. I added conditional compilation to allow you to further reduce the amount of RAM (i.e. data space) needed
by μC/OS-II. This is especially useful when you have resource limited products. I also added the feature described in
the previous section and cleaned up the code.
Where the book is concerned, I wanted to clarify some of the concepts described in the first edition and provide
additional explanations about how μC/OS-II works. I had numerous requests about doing a chapter on how to port
μC/OS and thus, such a chapter has been included in this book for μC/OS-II.
