ReBEL is a Matlabtoolkit of functions and scripts, designed to
facilitate sequential Bayesian inference (estimation) in general state
space models. This software consolidates research on new methods for
recursive Bayesian estimation and Kalman filtering by Rudolph van der
Merwe and Eric A. Wan. The code is developed and maintained by Rudolph
van der Merwe at the OGI School of Science & Engineering at OHSU
(Oregon Health & Science University).
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．
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)
Ink Blotting
One method for escaping from a maze is via ‘ink-blotting’. In this method your starting square
is marked with the number ‘1’. All free, valid squares north, south, east and west around the
number ‘1‘ are marked with a number ‘2’. In the next step, all free, valid squares around the two
are marked with a ‘3’ and the process is repeated iteratively until :
The exit is found (a free square other than the starting position is reached on the very edge
of the maze), or,
No more free squares are available, and hence no exit is possible.
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.
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.
This example demo code is provided as is and has no warranty,
implied or otherwise. You are free to use/modify any of the provided
code at your own risk in your applications with the expressed limitation
of liability (see below) so long as your product using the code contains
at least one uPSD products (device).
1) Write a function reverse(A) which takes a matrix A of arbitrary dimensions as input and returns a matrix B consisting of the columns of A in reverse order. Thus for example, if
A = 1 2 3 then B = 3 2 1
4 5 6 6 5 4
7 8 9 9 8 7
Write a main program to call reverse(A) for the matrix A = magic(5). Print to the screen both A and reverse(A).
2) Write a program which accepts an input k from the keyboard, and which prints out the smallest fibonacci number that is at least as large as k. The program should also print out its position in the fibonacci sequence. Here is a sample of input and output:
Enter k>0: 100
144 is the smallest fibonacci number greater than or equal to 100.
It is the 12th fibonacci number.
