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Dr.-Ing. Erwin SitompulPresident University
Lecture 1
Introduction to Neural Networksand Fuzzy Logic
President University Erwin Sitompul NNFL 1/1
http://zitompul.wordpress.com
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President University Erwin Sitompul NNFL 1/2
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.
TextbooksIntroduction to Neural Networks and Fuzzy Logic
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President University Erwin Sitompul NNFL 1/3
Grade PolicyIntroduction to Neural Networks and Fuzzy Logic
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 penalty10 – 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.
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President University Erwin Sitompul NNFL 1/4
Grade PolicyIntroduction to Neural Networks and Fuzzy Logic
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.
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President University Erwin Sitompul NNFL 1/5
Introduction to Neural Networks
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.
Theoretical modeling
Experimentalmodeling validation
measurement
Empirical Phenomenon
Mathematical Model
Data
IntroductionNeural Networks
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President University Erwin Sitompul NNFL 1/6
Experimental ModelingExperimental modeling consists of three steps:
1. The choice of model class2. 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.
IntroductionNeural Networks
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President University Erwin Sitompul NNFL 1/7
Experimental Modeling Using Neural NetworksA 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.
IntroductionNeural Networks
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President University Erwin Sitompul NNFL 1/8
Biological and Artificial Neuron
soma
synapse
dendrite
axon
Structure of Biological neuron
ky( )f
1kw
2kw
kmwkb
1x
mx
2x
1
net
Structure of Artificial neuron
( )ky f net1
m
ki i ki
net w x b
Activation function
IntroductionNeural Networks
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President University Erwin Sitompul NNFL 1/9
Activation Function
y
x
( )y f x x
1
y
x
22( ) 1
1 xy f x
e
1y
x
1( )1 xy f xe
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
x
2( ) axy f x e
Linearfunction
Tangentsigmoidfunction
Logarithmicsigmoidfunction
Radial basisfunction
IntroductionNeural Networks
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President University Erwin Sitompul NNFL 1/10
Network ArchitecturesSingle layer feedforward network(Single layer perceptron)
Inputlayer
Outputlayer
Multilayer feedforward network(Multilayer perceptron)
Inputlayer
Outputlayer
Hidden
layer
IntroductionNeural Networks
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President University Erwin Sitompul NNFL 1/11
Network Architectures
1z
1z
1z1z
Diagonal recurrent networks
Inputlayer
Outputlayer
Hiddenlayer
Inputlayer
Outputlayer
Hiddenlayer
Delay element in arecurrent network
Fully recurrent networks
IntroductionNeural Networks
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President University Erwin Sitompul NNFL 1/12
Network Architectures
1z
1z
1z
1z
1z
1z
Elman’s recurrent networks Jordan’s recurrent networks
IntroductionNeural Networks
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President University Erwin Sitompul NNFL 1/13
Preparation AssignmentIntroductionNeural Networks
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.