Soft computing
AI and Soft computing
Biological network
Neural Networks
Fuzzy Set Theory
“
Soft computing is a collection of methodologies that aim to
exploit the tolerance for imprecision and uncertainty to
achieve tractability, robustness, and low solution cost.
Its principal constituents are fuzzy logic, neurocomputing,
and probabilistic reasoning. Soft computing is likely to play
an increasingly important role in many application areas,
including software engineering. The role model for soft
computing the human mind
.”
ANN
Learning and adaptation
Fuzzy Set Theory
Knowledge representation Via
Fuzzy if-then RULE
ANN
Learning and adaptation
Fuzzy Set Theory
Knowledge representation Via
Fuzzy if-then RULE
Genetic Algorithms Systematic Random Search
cat cut
knowledge
Animal? cat
Conventional AI:
◦
Focuses on attempt to mimic human intelligent behavior
by expressing it in language forms or symbolic rules
◦
Manipulates symbols on the
assumption
that such
behavior can be stored in symbolically structured
knowledge bases (
physical symbol system hypothesis
)
◦
“AI is the activity of providing such machines as
computers with the ability to display behaviours that
would be regarded as intelligent if it were observed in
humans” (R. McLeod)
A part of the goal of studying Neural Networks is to learn the mechanism of our brain.
Neural Network is made up of neurons and synapses.
We have many variants of Neural Networks, based on how neurons are connected.
In this course, however, we employ Neural Networks as a black box which has a number of inputs and outputs. The task is to classify objects.
For example,
• We can recognize handwritten characters by giving pixel values as inputs
• We can classify coins inserted into Coke-machine by giving some features like diameter and weight of the coin as inputs.
• We can identify a jet fighter as enemies' by a set of data from radar image.
All what we have to do is to determine the strength of connection of every synapses
called synaptic weight.
For the purpose, we adjust each of the weight values starting with a set of random
The human brain has about 10
11neurons and 10
14synapses.
A neuron consists of a soma (cell body), axons
(sends signals), and dendrites (receives signals).
A synapse connects an axon to a dendrite.
Given a signal, a synapse might increase (excite)
or decrease(inhibit) electrical potential.
A neuron fires when its electrical potential
reaches a threshold.
NN consists of many number of simple
elements (neurons) connected between them in
system
Whole system is able to solve of complex tasks
and to learn for it like a natural brain
For user NN is black box with Input vector
(source data) and Output vector (result)
Examples of tasks:
Recognition of images (visual, speech and so on)
Prediction of situations (cost of actions, currency)
Classification and clusterization of images (for
An (artificial) neural network consists of
units, connections, and weights. Inputs and
outputs are numeric.
Biological NN Artificial NN
soma unit
axon, dendrite connection
synapse weight
potential weighted sum
threshold bias weight
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Control
ANN
G
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G
c(
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R
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C(s
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+
-+
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Plant
Feedforward
controller
Feedback controller
ANN
-+
Control
Ball-position
sensor
Controller
Current-driven
magnetic field
What is fuzzy thinking
◦ Experts rely on common sense when they solve the problems.
◦ How can we represent expert knowledge that uses vague and ambiguous terms in a computer
◦ Fuzzy logic is not logic that is fuzzy but logic that is
used to describe the fuzziness. Fuzzy logic is the theory of fuzzy sets, set that calibrate the vagueness.
◦ Fuzzy logic is based on the idea that all things admit of degrees. Temperature, height, speed, distance, beauty – all come on a sliding scale.
Jim is tall guy
Communication of “fuzzy “ idea
This box is
too heavy.. Therefore, we need a lighter
Boolean logic
◦
Uses sharp distinctions. It forces us to draw a
line between a members of class and non
members.
Fuzzy logic
◦
Reflects how people think. It attempt to model
our senses of words, our decision making and
our common sense -> more human and
Who is greater than 1.80 m?
Who is tall?
Who weighs more than 100 kg?
Who is heavy?
Classical Set vs Fuzzy set
No
Name
Height
(cm)
Degree of Membership
of “tall men”
Crisp
Fuzzy
1
Boy
206
1
1
2
Martin
190
1
1
3
Dewanto
175
0
0.8
4
Joko
160
0
0.7
Classical Set vs Fuzzy set
1
0
175 Height(cm)
1
0
175 Height(cm)
Universe of discourse
Classical Set vs Fuzzy set
A x A x x f X xfA A
if , 0 if , 1 ) ( where }, 1 , 0 { : ) ( ) (x A
Let X be the universe of discourse and its elements be denoted as x.
In the classical set theory, crisp set A of X is defined as function fA(x) called the the characteristic function of A
In the fuzzy theory, fuzzy set A of universe of discourse X is defined by function called the membership function of set A
Membership function
0 2 4 6 8 10
0.2 0.4 0.6 0.8 1
Derajat keanggotaan [0, 1]
elemen semesta pembicaraan A
0 1
0.5
c-b c-b/2 c c+b/2 c+b
EA FL
ANNs AI
50s 56 AI 57 Perceptron
40s 47 Cybernetics 43 Neuron Model
60s 60 Lisp Adaline - Madaline 65 Fuzzy Sets
70s Expert Systems 74 Back-Propagation 74 Fuzzy Control Genetic Algorithm
80s Immune modelling
85 Fuzzy modelling (TSK model)
80 Self orgazing map
82 Hopfield 83 Boltzmann Mach
90s Genetic
Fuzzy Expert Systems
Kecepatan (KM)
Jarak (JM)
