Neural and evolutionary computing is an emerging technology in advanced
computer-applications. It is about using computers to learn, optimise and discover new
engineering solutions and technologies by mimicking human intelligence in the brain and
genetic systems. It has been proven very useful in learning and automating (as well as
discovering new) designs for electronic/electrical systems, circuits, filters, motors,
drives and control systems. This course however requires little mathematics or
programming.
I. Laboratory and Distance-Learning/Experimentation
Experiment 1: What Evolutionary Computing is/How a Genetic Algorithm Works?
Press here for an interactive answer and hands-on excercise.
Experiment 2: Solving the Travelling Salesman's Problem by Learning
Executable DOS file developed by an NEC4'95 student and the Lecturer: nec4tsp.exe
Source code in C (Travelling Salesman's Problem and Simulated Annealing): nec4tsp.cpp
Borland C++ IDE (integrated development environment) file: nec4tsp.ide
Experiment 3: MATLAB Electronic Tutorials on WWW
Thanks to University of Michigan and Carnegie Mellon University, you can now carry out
"distance-learning" and self-tutorials of MATLAB by following one of these
links:
With access to both the WWW and to Matlab on either a personal computer or a
workstation, any student will be able to follow a tutorial while running Matlab and will
be able to easily switch between the two programs. Students will be able to grasp key
concepts and design techniques in a "learn by seeing and doing" manner.
Commands shown in the tutorials can be copied from the Netscape, MSIE(?) or Mosaic
window and pasted into the Matlab command window or into an m-file with a simple point and
click of the mouse; there is no need for time-consuming typing and editing. Students can
immediately see the result of an actual computation, compare it to the result shown on the
tutorial, and quickly experiment with modifications of the commands and changes of
parameters.
You can escape from the above just now, and only follow the instructions and simple
MATLAB code provided in the Laboratory Sheets. Also try to follow the on-line help in the
MATLAB window and consult the "command sheets" and manuals provided in the
Laboratory. If you wish to download (and not to type in) the m-file used for the 3-D and
its contour plots, multipk.m,
click here and save it in your c:\matlab\bin directory.
Follow the instructions given in the Laboratory Sheets.
FlexTool(GA) Toolbox
Exhaustive search and Optimisation
1-D Single Objective Single-Peak and Multi-Peak Problems
2-D and Multiple Objectives
Experiment 5: Neural Network Toolbox and Neural Learning
Follow the instructions given in the Laboratory Sheets.
Backpropagation Learning
Application in Noise Cancellation
Hebbian Learning
Solving Neural Learning Problems in Tutorial 2
Experiment 6: Neural Learning and Fuzzy Systems - IEE/IEEE Video Tape and CD-ROMs
(Optional and during spare time only)
The IEE Video Tape shown at the lecture can be viewed again from the CD-ROM: "IEE
Distance Learning - Neural Computing Video Course (SO2). You can run this on the Pentium
133 PCs, which have a sound card. Ask Mr. O'Hara, the Technician, for a pair of speakers.
You can also use the CD to see the demonstration on how neural networks work. Note that
other options do not work on the CD!
You can also borrow the IEEE/ABAS "Learning Neural Networks" CD-ROM from Mr.
O'Hara.
"1993 IEEE International Conference on Neural Networks" CD-ROM also
available.
"Second IEEE International Conference on Fuzzy Systems" CD-ROM also
available.
II. Further Information
This page must not be used alone and must be used in conjunction with NEC4 Lecture
Notes, Appendices, Tutorial Sheets, Laboratory Sheets and Assignment Sheets. The IEE and IEEE video tape and CD-ROMs used in the course may be borrowed
from the Lecturer, Dr. Y. Li.
Start to think about it now! You are also highly encouraged to propose your own
mini-project.
Completed assignment must be submitted to Dr. Li (into a
"run-about" mailbox just outside B1-16) by the end of Week 12, Friday, 23
January 1998.
If you have genuine difficulties beyond your control, however,
you may be allowed to submit the entire mini-project by Week 13, but you must contact Dr. Li in the first instance to submit an outline
plan of your mini-project by Week 11. Submitting a floppy containing your programs is
voluntary, but you may be asked to do so for random inspection.
If your report is of value for future teaching, we would like to ask for your
contribution to this course the next year.
All materials provided by this site are copyright protected and are for use with Glasgow University's Neural and
Evolutionary Computing IV (1995-97) course only. It is illegal to copy, use or
distribute any such material for any other purposes without the consent of the course
Lecturer, Dr. Y. Li.
