Fuzzy, Expert, Genetic
and Neural Systems
Last updated 6/8/07.
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Prerequisites:
Programming in Java or C++; recommended that students will have taken the core required
courses for the MS degree in computer science.
Course
description: Theories and methods for automating the solution of problems with
inexact specifications, input, processing models or output. (e.g.
text checkers, user profiles, help desks, intelligent agents). Expert systems,
fuzzy methods, neural nets and genetic algorithms are described and compared.
Algorithms and a term project are implemented using shells, C++ or Java.
·
Understand the goals, capabilities and limitations
of soft computing
·
Be familiar with Expert Systems, Neural Nets, Fuzzy
systems, and Genetic Algorithms
· Be able to select
among these given an application
The
instructor will provide copies of presentation material for all classes.
Textbook:
"Soft Computing
and Intelligent Systems Design: Theory, Tools and Applications” by Fakhreddine O. Karray and
Clarence W De Silva; ISBN-10: 0321116178; ISBN-13: 978-0321116178
Students will
probably want to acquire resources particular to the area on which they intend
to focus.
Students
will choose one or -- preferably -- a combination of two of the four areas in
which to design and execute a project, and can purchase recommended literature
accordingly.
Past students in this course
have begun to develop a list of references and tools at http://metcs.bu.edu/~ebraude/767/articles/index.htm . See also the forums.
See also http://jooneworld.com/index.html for a good
neural net framework.
The
course will consist of homework and a project, weighted as follows.
The
project will be in three phases, weighted as follows:
phase 1
(problem statement): -- 1/6
phase 2
(analysis & design): -- 1/3
phase 3
(implementation and critical review): --1/2
Parts of
assignments are evaluated equally unless otherwise stated.
Students
may be permitted to substitute parts of these with a special paper, approved in
advance within the first 3 weeks. Late homework without a reason why it was
impossible will not be accepted. If there is such impossibility, the work will
be graded on a pass/fail basis. Reasons
should be clearly written on the front of the paper. The fax (617) 353-2367 should be used if you
cannot be at class.
See further details on the grading system used.
Students
are required to make a presentation on their project. The suggested organization is as follows.
1. Project goals
(application and learning)
2. Method and design
3. Outcomes: challenges, difficulties and problems
4. Outcomes: successes
5. What would be required to make real
Since these are cutting-edge topics, the syllabus may be adjusted somewhat
during the semester.
|
Class Num |
Date |
Topic |
Notes
and Related
|
Comments The due dates mentioned below are only
approximate. For finalized due dates click here. |
|
1 |
5/22 |
Introduction
to Soft Computing I Contrast expert systems and fuzzy systems |
Karray-DeSilva 1 |
think
about project |
|
2 |
5/29 |
Introduction
to Soft Computing II Contrast expert systems, neural nets, fuzzy
systems, and genetic algorithms |
Karray-DeSilva 1
|
Project: Phase 1
assigned |
|
3 |
6/5 |
Introduction
to Expert Systems Define expert systems; knowledge representation Inference
in Expert Systems: Part I Using rules & decision trees |
|
|
|
4 |
6/12 |
Inference in Expert Systems: Part II Knowledge
Acquisition; Processing uncertainty; preliminary comparison
with fuzzy systems |
|
Phase 1 due; phase 2 assigned |
|
5 |
6/19 |
Introduction to
Fuzzy Systems Define; basic set theory; describe applications |
Karray-DeSilva 2 |
|
|
6 |
6/26 |
To be decided |
|
Phase 3 assigned Phase 2 due |
|
7 |
7/3 |
Implementation of
Fuzzy Systems
Architectures & tools |
Karray-DeSilva 3 |
|
|
8 |
7/10 |
Introduction to Neural Nets Basic architectures |
Karray-DeSilva 4 |
|
|
9 |
7/17 |
Backpropagation
Define and use the algorithm
|
Karray-DeSilva 5 and 6 |
|
|
10 |
7/26 |
Introduction
to Genetic Algorithms Define
and use genetic algorithms |
Karray-DeSilva 8 |
|
|
11 |
7/31 |
Genetic Algorithms and
Evolutionary Computations |
Karray-DeSilva 8 |
Project: Phase 3 due |
|
12 |
8/7 |
Student
Presentations and Demonstrations |
|
|
The
College has serious penalties for plagiarism, including expulsion from the
degree program. Please be very careful not to use the work of others without
very clear and specific acknowledgement.
e-mail, see or call me if you have any doubts. In any
case, clearly acknowledge all sources in the context they are used, including
code, of course. Please see plagiarism
policies (my hints on this) and here (MET College) for examples and a
fuller explanation.
Forum
Past
forums:
1999 ,2000,
Summer
2002, Spring 2003, Fall 2003,
Summer 2005
Summer 2007:
Group
name: 767Su07
Group
home page: http://groups.yahoo.com/group/767Su07
Group
email: