Introduction to Artificial Neural Networks - PART II.pdf

AN INTRODUCTION TO
ARTIFICIAL NEURAL NETWORKS
PART - II
Dr.S.SASIKALA
Department of ECE
Kumaraguru College of Technology
Coimbatore
Department of
Electronics and Communication Engineering
Since 1986
August 27, 2022
IEEE EAB & IEEE MAS Sponsored
TryEngineering Workshop on Artificial
Intelligence for All
1

BIOLOGICAL AND ARTIFICIAL
NEURAL NETWORKS
August 27, 2022
2

Biological Neuron
Cell body(Soma)
• Containing organelles of the neuron
Dentrites (Rx)
• Tree-like structure originating to cell body that
receives the signal from surrounding neurons
Axon (TX)
• Long connection extending from cell body and carries signal
• There is only one axon per neuron that axon may divide in many branches at its end
and connected to other cells to transmits the signal from one neuron to others
Synapse
• Small-bulb like organ neuron at the end of axon which introduces the signal to the
near by dendrites of the other through chemical diffusion
Neuron
• Summed up all the inputs and process the sum by a threshold function and
produces an output signal.
• A neuron fires an electrical impulse only if certain condition is met
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Biological Neural Network
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How do you model an Artificial Neuron
By simulating functioning of a biological neuron
❑Function 1 – Accumulation of Information
Summation or Net Input Calculation
❑Function 2 – Passing of Information
Threshold or Activation or Producing output
Simulation involves
❑Identify the equivalent mathematical operator for the function
❑Design a mathematical model that process information
Artificial Neuron Resembles the human brain in two respects:
❑Knowledge acquisition through learning
❑Storage of knowledge in the synaptic weights
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Biological Neuron and Artificial Neuron
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Biological & Artificial Neuron
Resemblance
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7

ANN vs BNN
BNN ANN
Soma Node
Dendrites Input
Synapse Weights or Interconnections
Axon Output
Massively parallel, slow but superior than
ANN
Massively parallel, fast but inferior than BNN
10
11
neurons and 10
15
interconnections 10
2
to 10
4
nodes mainly depends on the type
of application and network designer
They can tolerate ambiguity Very precise, structured and formatted data
is required to tolerate ambiguity
Performance degrades with even partial
damage
It is capable of robust performance, hence
has the potential to be fault tolerant
Stores the information in the synapse Stores the information in continuous
memory locations
August 27, 2022
8

ANN - Function
-
f
Weighted
sum
Input
vector x
Output y
Weight
vector
w

w0j
w1j
wnj
x0
x1
xn
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What is ANN
Artificial Neuron
➢A digital construct that seeks to simulate the behavior of a
biological neuron in the brain.
➢They may be physical devices, or purely mathematical
constructs.
Artificial Neural Networks (ANN)
➢ Networks of Artificial Neurons
➢A parallel computational system consisting of a huge number
of simple and massively connected processing elements
connected together in a specific manner in order to perform a
particular task
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History of ANN
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Model of Artificial Neural Network
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Model of Artificial Neural Network
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• In the general model of ANN, the net input is
calculated by using the equation
• The output can be calculated by applying the
activation function over the net input

ANN - Building Blocks
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CLASSIFICATIONS OF ANN
• Based on the architecture
➢Feed Forward Neural Network (FFNN)
➢Feed Back Neural Network (FBNN)
➢Recurrent Neural Network (RNN)
➢Competitive Neural Network (CNN)
• Based on the learning algorithm
➢Supervised Learning
➢Unsupervised Learning
➢Reinforcement Learning
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Activation Functions
➢Activation functions are mathematical equations i.e a non-linear
transformations attached to each neuron in the network, which
determines whether the neuron should be activated (“fired”)
or not by calculating weighted sum and further adding bias with
it.
➢The purpose of the activation function is to introduce non-
linearity into the output of a neuron.
➢Activation functions also help normalize the output of each
neuron to a range between 1 and 0 or between -1 and 1.
➢The activation function does the non-linear transformation to
the input making it capable to learn and perform more complex
tasks.
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Activation Functions
Linear Activation Function or identity function
Sigmoid Activation Function
➢Binary sigmoidal function
➢Bipolar sigmoidal function
F(x) = 1 if x > 0 else 0 if x < 0
Binary Step Activation Function
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August 27, 2022
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