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EMEC -
5173
Intelligent Tools for Engineering
Applications
Instructor: Dr. Wilson Wang,
CB-4057,
766-7174
Email: Wilson.Wang@Lakeheadu.ca
Webpage: http://wwang3.lakeheadu.ca/emec5173.htm
Lectures: 5:30-7:00PM,
Monday & Wednesday, AT-2005
Office Hours:
1:00-2:00PM, Monday
Teaching
Assistant:
Mahsa <mjahed@lakeheadu.ca>
Reading Materials:
-
Soft Computing and Intelligent
Systems Design: Theory, Tools, and Applications, F. Karray, C. deSilver,
Pearson Publishing Inc., 2004.
-
Neuro-Fuzzy and Soft
Computing, J. R. Jang, C. Sun, and E. Mizutani, Printice Hall, 1997
Objective:
Computational intelligence
and soft computing have become the subject of rapidly growing interest in a wide
range of scientific research and engineering applications including consumer
products, mechatronic systems, industrial process control, information systems,
pattern recognition, system state prediction, etc. This course discusses
fundamentals of intelligent systems design using soft computing tools including
approximate reasoning and fuzzy logic, neural networks, hybrid techniques (e.g.
neuroİ\fuzzy schemes, machine learning, and some applications especially in
system state forecasting. MATALB or other related software package can be used for programming.
Course Outline:
- Introduction
Fuzzy
set theory
Fuzzy rules and inference systems
Neural networks
System
training
Synergetic schemes
Applications (forecasting, pattern classification,
control)
Student Learner Outcomes
Grading Policy:
Assignments:
10%
Project:
20%
Midterm
exam: 25%
Final exam:
45%
|
Week |
Topics |
Book #1 |
Book #2 |
Assignments |
|
1 |
Introduction; Crisp logic |
1.1-1.4 |
|
Assignment 1 |
|
2 |
Fuzzy sets; Fuzzy logic |
2.1-2.2 |
2,1-2.2 |
|
|
3 |
Fuzzy logic operations |
2.3-2.4 |
2.2-2.3 |
Assignment 2 |
|
4 |
MFs;
Generalized fuzzy operations; Implication; Extension principle |
2.5-2.7; 2.9 |
2.4.1; 3.1 |
Solution 1 |
|
5 |
Fuzzy
if-then rules; Fuzzy reasoning, LSE |
|
3.2-3.4;
4.1-4.5 |
Solution 2 |
|
6 |
Recursive LSE; Gradient algorithm |
|
5.1-5.4; 6.1-6.2 |
Assignment 3 |
|
7 |
Reading week |
|
|
|
|
8 |
Gradient search; GA |
|
6.3 |
Solution 3 |
|
9 |
NNs;
Connectionist modeling |
4.4-4.5; 5.1-5.3 |
|
Project |
|
10 |
Multilayer
perceptrons |
5.1-5.3
6.1-6.4 |
|
Assignment 4 |
|
11 |
BFN;
Neuro-fuzzy systems |
6.1-6.4; 7.1-7.2 |
12.1-12.2 |
Solution 4 |
|
12 |
ANFIS;
System training |
7.3-7.5 |
12.3-12.6 |
Assignment 5
|
|
13 |
Application examples; Evolving fuzzy systems |
3.1-3.3; 3.5
6.5-6.6 |
|
Solution 5 |
|