Introduction to Neural Networks
and Fuzzy Logic
Lecture 1
Dr.-Ing. Erwin Sitompul
President University
http://zitompul.wordpress.com
President University
Erwin Sitompul
NNFL 1/1
Introduction to Neural Networks and Fuzzy Logic
Textbooks
Textbook:
“Neural Networks. A Comprehensive
Foundation”, 2nd Edition, Simon Haykin,
Prentice Hall, 1999.
“Fuzzy Systems Theory and Its Application”,
Toshiro Terano et. al., Academic Press, 1992.
President University
Erwin Sitompul
NNFL 1/2
Introduction to Neural Networks and Fuzzy Logic
Grade Policy
Final Grade = 20% Homework + 20% Quizzes +
30% Midterm Exam + 30% Final Exam +
Extra Points
 Homeworks will be given in fairly regular basis. The
average of homework grades contributes 20% of final
grade.
 Homeworks are to be submitted on A4 papers,
otherwise they will not be graded.
 Homeworks must be submitted on time. If you submit
late,
< 10 min.
 No penalty
10 – 60 min.  –20 points
> 60 min.
 –40 points
 There will be 3 quizzes. Only the best 2 will be counted.
The average of quiz grades contributes 20% of final
grade.
President University
Erwin Sitompul
NNFL 1/3
Introduction to Neural Networks and Fuzzy Logic
Grade Policy
 Midterm and final exam schedule will be announced in
time.
 Make up of quizzes and exams will be held one week
after the schedule of the respective quizzes and exams.
 The score of a make up quiz or exam, upon discretion,
can be multiplied by 0.9 (the maximum score for a
make up is then 90).
 Extra points will be given if you solve a problem in front
of the class. You will earn 1, 2, or 3 points.
 You are responsible to read and understand the
lecture slides. I am responsible to answer your
questions.
President University
Erwin Sitompul
NNFL 1/4
Neural Networks
Introduction
Introduction to Neural Networks
Empirical
Phenomenon
measurement
Data
Experimental
modeling
Theoretical
modeling
validation
Mathematical
Model
Validation:
• Generally, means confirming that a
product or service meets the needs of
its users.
• Testing whether the mathematical
model is good enough or not to
describe the empirical phenomenon.
President University
Erwin Sitompul
NNFL 1/5
Neural Networks
Introduction
Experimental Modeling
 Experimental modeling consists of three steps:
1. The choice of model class
2. The choice of model structures (number of
parameters, model order, time delay)
3. The calculation of the parameters and time delay.
 The model may be chosen to be linear, nonlinear, or
multi locally-linear.
 A-priori (prior, previous) knowledge of the system to be
modeled is required in most cases.
 Artificial Neural Networks (or simply Neural
Networks) offers a general solution for experimental
modeling.
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Erwin Sitompul
NNFL 1/6
Neural Networks
Introduction
Experimental Modeling Using Neural Networks
 A neural network is a massively-parallel distributed
processor made up of simple processing unit, which has
natural propensity for storing experiential knowledge
and making it available for use.
 It resembles the brain in two respects:
1. Knowledge is acquired by the network from its
environment through a learning process.
2. Interneuron connection strengths, known as
synaptic weights, are used to store the acquired
knowledge.
President University
Erwin Sitompul
NNFL 1/7
Neural Networks
Introduction
Biological and Artificial Neuron
dendrite
Structure of
Biological neuron
soma
axon
synapse
x1
Structure of
Artificial neuron
Activation
function
wk1
x2
wk 2
wkm

yk
f()
net
bk
m
net 
xm
1
w
ki
x i  bk
i 1
y k  f ( net )
President University
Erwin Sitompul
NNFL 1/8
Neural Networks
Introduction
Activation Function
 Any continuous (differentiable) function can be used as
an activation function in a neural network.
 The nonlinear behavior of the neural networks is
inherited from the used nonlinear activation functions.
y
y
y
1
1
x
x
y  f ( x)  x
y
y  f ( x) 
Linear
function
President University
2 1
1 e2 x
Tangent
sigmoid
function
x
x
y  f ( x) 
1
1  e x
Logarithmic
sigmoid
function
Erwin Sitompul
y  f ( x)  eax
2
Radial basis
function
NNFL 1/9
Neural Networks
Introduction
Network Architectures
Single layer feedforward network
(Single layer perceptron)
Input
layer
Output
layer
Multilayer feedforward network
(Multilayer perceptron)
Input
layer
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Erwin Sitompul
Hidden
layer
Output
layer
NNFL 1/10
Neural Networks
Introduction
Network Architectures
Diagonal recurrent networks
Input
layer
Hidden
layer
Output
layer
Fully recurrent networks
Input
layer
Hidden
layer
Output
layer
z 1
z 1
Delay element in a
recurrent network
President University
Erwin Sitompul
z 1
z 1
NNFL 1/11
Neural Networks
Introduction
Network Architectures
Elman’s recurrent networks

z

Jordan’s recurrent networks



z 1
1
z 1

z

 
1
z 1
z 1
President University
Erwin Sitompul
NNFL 1/12
Neural Networks
Introduction
Preparation Assignment
 Ensure yourself to install Matlab 7 in your computer,
along with Matlab Simulink, Control System Toolbox,
and Fuzzy Logic Toolbox.
 Quizzes, Midterm Exam, and Final Exam will be
computer-based.
President University
Erwin Sitompul
NNFL 1/13