COURSE DESCRIPTION
SysEng/CS 378 and El Eng 368 – Introduction to Neural Networks and Applications
(Offered Spring Semesters)
Required or Elective Course: Elective
Catalog Description:
(Lec. 3.0) Introduction to artificial neural network architectures, adaline,
madaline,back propagation, BAM, and Hopfield memory, counterpropagation
networks, self organizing maps, adaptive resonance theory, are the topics covered.
Students experiment with the use of artificial neural networks in engineering through
semester projects. Prerequisite: Math 204 or 229 Differential Equations. (Co-listed
with Cp Sc 378 and Sys Eng 378)
Prerequisites by topic: Matrix Algebra and Differential Equations
Textbooks and other required material:
M. H. Ham and I. Kostanic, Principles of Neurocomputing for Science and
Engineering, (McGraw-Hill, NY, NY, 2001).
Duane C. Hanselman, Bruce Littlefield, and Bruce L. Littlefield, Mastering Matlab 7, (Prentice
Hall, NJ, 2004).
MatLab and Neural Networks Tool Box (Software)
Course learning outcomes/expected performance criteria:
1.
2.
3.
4.
5.
Learn basic neural network architecture
Learn basic learning algorithms
Understand data pre and post processing
Learn training, verification and validation of neural network models
Design Engineering applications that can learn using neural networks
Topics covered:
1. Introduction (1 week)
2. Network Architectures and MatLab Basics (1 week)
3. Linear Algebra Review and Adaline (1 weeks)
4. MatLab Neural Network Toolbox and Madaline (1 week)
5. Perceptron and Learning Rules for a Single Neuron (1 week)
6. Associative Memories (1 week)
7. Backpropagation Learning Algorithm (3 weeks)
8. Radial Basis Function and Self-Organizing Networks (1 week)
9. Learning Vector Quantization and Adaptive Resonance Theory (2 weeks)
10. Project (1 week)
11. Reviews, Examinations (3 weeks)
Class/laboratory schedule:
One 150-minute lecture per week is typical MatLab is used throughout the lectures,
examinations, and homework.
Contribution of course to meeting the professional component:

Students are exposed to neural network architectures and their applications in
engineering design
Relationship of course learning outcomes to ECE program outcomes:
ECE
Outcome
a
Course Outcomes
1
2
3
4
5
S
S
S
M
S
b
c
d
e
f
g
h
i
j
k
l
S
Comments
Students use optimization tools in developing
learning algorithms
S
S
S
S
S
S
M
S
S
Students designs engineering applications that
can adapt and learn
Students presents engineering applications
S
S – strong connection; M – medium connection; W – weak connection
Prepared by: Cihan H Dagli
Date: February 1, 2008