ppt_artificial neural netwroK_module 3_KTU

Artificial Neural Networks : An
Introduction
Module - 3

Learning Objectives
• Fundamentals of ANN
• Comparison between biological neuron
and artificial neuron
• Basic models of ANN
• Different types of connections of NN,
Learning and activation function
• Basic fundamental neuron model-
McCulloch-Pitts neuron and Hebb network

Fundamental concept
• NN are constructed and implemented to
model the human brain.
• Performs various tasks such as pattern-
matching, classification, optimization
function, approximation, vector
quantization and data clustering.
• These tasks are difficult for traditional
computers

ANN
• ANN posess a large number of processing
elements called nodes/neurons which operate in
parallel.
• Neurons are connected with others by
connection link.
• Each link is associated with weights which
contain information about the input signal.
• Each neuron has an internal state of its own
which is a function of the inputs that neuron
receives- Activation level

Artificial Neural Networks
x1
x2
X1
X2
w1
w2
Y y
n
X
1 1 2 2
in
y x w x w
 
( )
in
y f y


Neural net of pure linear eqn.
Y
X
Input
m
mx

Information flow in nervous system

Neuron and a sample of pulse train

Biological Neuron
• Has 3 parts
– Soma or cell body:- cell nucleus is located
– Dendrites:- nerve connected to cell body
– Axon: carries impulses of the neuron
• End of axon splits into fine strands
• Each strand terminates into a bulb-like organ called synapse
• Electric impulses are passed between the synapse and dendrites
• Synapses are of two types
– Inhibitory:- impulses hinder the firing of the receiving cell
– Excitatory:- impulses cause the firing of the receiving cell
• Neuron fires when the total of the weights to receive impulses
exceeds the threshold value during the latent summation period
• After carrying a pulse an axon fiber is in a state of complete
nonexcitability for a certain time called the refractory period.

Features of McCulloch-Pitts model
• Allows binary 0,1 states only
• Operates under a discrete-time
assumption
• Weights and the neurons’ thresholds are
fixed in the model and no interaction
among network neurons
• Just a primitive model

General symbol of neuron
consisting of processing node and
synaptic connections

Neuron Modeling for ANN
Is referred to activation function. Domain is
set of activation values net.
Scalar product of weight and input vector
Neuron as a processing node performs the operation of summation of
its weighted input.

Activation function
• Bipolar binary and unipolar binary are
called as hard limiting activation functions
used in discrete neuron model
• Unipolar continuous and bipolar
continuous are called soft limiting
activation functions are called sigmoidal
characteristics.

Activation functions
Bipolar continuous
Bipolar binary functions

Activation functions
Unipolar continuous
Unipolar Binary

Common models of neurons
Binary
perceptrons
Continuous perceptrons

Comparison between brain verses
computer
Brain ANN
Speed Few ms. Few nano sec. massive
||el processing
Size and complexity 1011 neurons & 1015
interconnections
Depends on designer
Storage capacity Stores information in its
interconnection or in
synapse.
No Loss of memory
Contiguous memory
locations
loss of memory may
happen sometimes.
Tolerance Has fault tolerance No fault tolerance Inf
gets disrupted when
interconnections are
disconnected
Control mechanism Complicated involves
chemicals in biological
neuron
Simpler in ANN

Basic models of ANN
Basic Models of ANN
Interconnections Learning rules Activation function

Classification based on
interconnections
Interconnections
Feed forward
Single layer
Multilayer
Feed Back Recurrent
Single layer
Multilayer

Single layer Feedforward
Network

Feedforward Network
• Its output and input vectors are
respectively
• Weight wij connects the i’th neuron with
j’th input. Activation rule of ith neuron is
where
EXAMPLE

Multilayer feed forward network
Can be used to solve complicated problems

Feedback network
When outputs are directed back as
inputs to same or preceding layer
nodes it results in the formation of
feedback networks

Lateral feedback
If the feedback of the output of the processing elements is directed back
as input to the processing elements in the same layer then it is called
lateral feedback

Recurrent n/ws
• Single node with own feedback
• Competitive nets
• Single-layer recurrent nts
• Multilayer recurrent networks
Feedback networks with closed loop are called Recurrent Networks. The
response at the k+1’th instant depends on the entire history of the network
starting at k=0.
Automaton: A system with discrete time inputs and a discrete data
representation is called an automaton

Learning
• It’s a process by which a NN adapts itself
to a stimulus by making proper parameter
adjustments, resulting in the production of
desired response
• Two kinds of learning
– Parameter learning:- connection weights are
updated
– Structure Learning:- change in network
structure

Training
• The process of modifying the weights in
the connections between network layers
with the objective of achieving the
expected output is called training a
network.
• This is achieved through
– Supervised learning
– Unsupervised learning
– Reinforcement learning

Classification of learning
• Supervised learning
• Unsupervised learning
• Reinforcement learning

Supervised Learning
• Child learns from a teacher
• Each input vector requires a
corresponding target vector.
• Training pair=[input vector, target vector]
Neural
Network
W
Error
Signal
Generator
X
(Input)
Y
(Actual output)
(Desired Output)
Error
(D-Y)
signals

Supervised learning contd.
Supervised learning
does minimization of
error

Unsupervised Learning
• How a fish or tadpole learns
• All similar input patterns are grouped together as
clusters.
• If a matching input pattern is not found a new
cluster is formed

Self-organizing
• In unsupervised learning there is no
feedback
• Network must discover patterns,
regularities, features for the input data
over the output
• While doing so the network might change
in parameters
• This process is called self-organizing

Reinforcement Learning
NN
W
Error
Signal
Generator
X
(Input)
Y
(Actual output)
Error
signals R
Reinforcement signal

When Reinforcement learning is
used?
• If less information is available about the
target output values (critic information)
• Learning based on this critic information is
called reinforcement learning and the
feedback sent is called reinforcement
signal
• Feedback in this case is only evaluative
and not instructive

1. Identity Function
f(x)=x for all x
2. Binary Step function
3. Bipolar Step function
4. Sigmoidal Functions:- Continuous functions
5. Ramp functions:-
Activation Function





ifx
ifx
x
f
0
1
{
)
(






ifx
ifx
x
f
1
1
{
)
(
0
0
1
0
1
1
)
(





ifx
x
if
x
ifx
x
f

Some learning algorithms we will
learn are
• Supervised:
• Adaline, Madaline
• Perceptron
• Back Propagation
• multilayer perceptrons
• Radial Basis Function Networks
• Unsupervised
• Competitive Learning
• Kohenen self organizing map
• Learning vector quantization
• Hebbian learning

Neural processing
• Recall:- processing phase for a NN and its
objective is to retrieve the information. The
process of computing o for a given x
• Basic forms of neural information
processing
– Auto association
– Hetero association
– Classification

Neural processing-Autoassociation
• Set of patterns can be
stored in the network
• If a pattern similar to
a member of the
stored set is
presented, an
association with the
input of closest stored
pattern is made

Neural Processing-
Heteroassociation
• Associations between
pairs of patterns are
stored
• Distorted input pattern
may cause correct
heteroassociation at
the output

Neural processing-Classification
• Set of input patterns
is divided into a
number of classes or
categories
• In response to an
input pattern from the
set, the classifier is
supposed to recall the
information regarding
class membership of
the input pattern.

Important terminologies of ANNs
• Weights
• Bias
• Threshold
• Learning rate
• Momentum factor
• Vigilance parameter
• Notations used in ANN

Weights
• Each neuron is connected to every other
neuron by means of directed links
• Links are associated with weights
• Weights contain information about the
input signal and is represented as a matrix
• Weight matrix also called connection
matrix

Weight matrix
W= 1
2
3
.
.
.
.
.
T
T
T
T
n
w
w
w
w
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
=
11 12 13 1
21 22 23 2
1 2 3
...
...
..................
...................
...
m
m
n n n nm
w w w w
w w w w
w w w w
 
 
 
 
 
 
 
 
 

Weights contd…
• wij –is the weight from processing element ”i” (source
node) to processing element “j” (destination node)
X1
1
Xi
Yj
Xn
w1j
wij
wnj
bj
0
0 0 1 1 2 2
0
1
1
....
n
i ij
inj
i
j j j n nj
n
j i ij
i
n
j i ij
inj
i
y xw
x w xw x w x w
w xw
y b xw




    
 
 




Activation Functions
• Used to calculate the output response of a
neuron.
• Sum of the weighted input signal is applied with
an activation to obtain the response.
• Activation functions can be linear or non linear
• Already dealt
– Identity function
– Single/binary step function
– Discrete/continuous sigmoidal function.

Bias
• Bias is like another weight. Its included by
adding a component x0=1 to the input
vector X.
• X=(1,X1,X2…Xi,…Xn)
• Bias is of two types
– Positive bias: increase the net input
– Negative bias: decrease the net input

Why Bias is required?
• The relationship between input and output
given by the equation of straight line
y=mx+c
X Y
Input
C(bias)
y=mx+C

Threshold
• Set value based upon which the final output of
the network may be calculated
• Used in activation function
• The activation function using threshold can be
defined as












ifnet
ifnet
net
f
1
1
)
(

Learning rate
• Denoted by α.
• Used to control the amount of weight
adjustment at each step of training
• Learning rate ranging from 0 to 1
determines the rate of learning in each
time step

Other terminologies
• Momentum factor:
– used for convergence when momentum factor
is added to weight updation process.
• Vigilance parameter:
– Denoted by ρ
– Used to control the degree of similarity
required for patterns to be assigned to the
same cluster

Neural Network Learning rules
c – learning constant

Hebbian Learning Rule
• The learning signal is equal to the
neuron’s output
FEED FORWARD UNSUPERVISED LEARNING

Features of Hebbian Learning
• Feedforward unsupervised learning
• “When an axon of a cell A is near enough
to exicite a cell B and repeatedly and
persistently takes place in firing it, some
growth process or change takes place in
one or both cells increasing the efficiency”
• If oixj is positive the results is increase in
weight else vice versa

• For the same inputs for bipolar continuous
activation function the final updated weight
is given by

Perceptron Learning rule
• Learning signal is the difference between the
desired and actual neuron’s response
• Learning is supervised

Delta Learning Rule
• Only valid for continuous activation function
• Used in supervised training mode
• Learning signal for this rule is called delta
• The aim of the delta rule is to minimize the error over all training
patterns

Delta Learning Rule Contd.
Learning rule is derived from the condition of least squared error.
Calculating the gradient vector with respect to wi
Minimization of error requires the weight changes to be in the negative
gradient direction

Widrow-Hoff learning Rule
• Also called as least mean square learning rule
• Introduced by Widrow(1962), used in supervised learning
• Independent of the activation function
• Special case of delta learning rule wherein activation function is an
identity function ie f(net)=net
• Minimizes the squared error between the desired output value di
and neti

Winner-Take-All learning rules

Winner-Take-All Learning rule
Contd…
• Can be explained for a layer of neurons
• Example of competitive learning and used for
unsupervised network training
• Learning is based on the premise that one of the
neurons in the layer has a maximum response
due to the input x
• This neuron is declared the winner with a weight

Linear Separability
• Separation of the input space into regions
is based on whether the network response
is positive or negative
• Line of separation is called linear-
separable line.
• Example:-
– AND function & OR function are linear
separable Example
– EXOR function Linearly inseparable. Example

Hebb Network
• Hebb learning rule is the simpliest one
• The learning in the brain is performed by the
change in the synaptic gap
• When an axon of cell A is near enough to excite
cell B and repeatedly keep firing it, some growth
process takes place in one or both cells
• According to Hebb rule, weight vector is found to
increase proportionately to the product of the
input and learning signal.
y
x
old
w
new
w i
i
i 
 )
(
)
(

Flow chart of Hebb training
algorithm
Start
Initialize Weights
For
Each
s:t
Activate input
xi=si
1
1
Activate output
y=t
Weight update
y
x
old
w
new
w i
i
i 
 )
(
)
(
Bias update
b(new)=b(old) + y
Stop
y
n

• Hebb rule can be used for pattern
association, pattern categorization, pattern
classification and over a range of other
areas
• Problem to be solved:
Design a Hebb net to implement OR
function

How to solve
Use bipolar data in the place of binary data
Initially the weights and bias are set to zero
w1=w2=b=0
X1 X2 B y
1 1 1 1
1 -1 1 1
-1 1 1 1
-1 -1 1 -1

Inputs y Weight changes weights
X1 X2 b Y W1 W2 B W1(0) W2(0) (0)b
1 1 1 1 1 1 1 1 1 1
1 -1 1 1 1 -1 1 2 0 2
-1 1 1 1 -1 1 1 1 1 3
-1 -1 1 -1 1 1 -1 2 2 2

Home work
• Using the hebb rule, find the weights
required to perform the following
classification that given input patterns
shown in figure

ppt_artificial neural netwroK_module 3_KTU

More Related Content

Similar to ppt_artificial neural netwroK_module 3_KTU

Recently uploaded

ppt_artificial neural netwroK_module 3_KTU