Artificial Neural Network

2. Contents
 Intoduction
 Artifcial Neural Network
 Biological Neuron Model
 Artificial Neuron Model
 Applications
 Advantages
 Disadvantages

3. Introduction
 Artificial Neural Network(ANN) is a computing
system made up of a number of simple, highly
interconnected processing elements, which process
information by their dynamic state response to
external inputs.
 They are desigend by inspiration from the
biological neural system

4. BIOLOGICAL NEURON MODEL
Four parts of a typical nerve
cell:-
DENDRITES: Accepts the
inputs
SOMA:Process the inputs
AXON:Turns the processed
inputs into outputs.
SYNAPSES:The
electrochemical contact
between the neurons

5. ARTIFICIAL NEURAL NETWORK
 Artificial Neural Network (ANNs) are programs
designed to solve any problem by trying to mimic the
structure and the function of our nervous system.
 Neural network resembles the human brain in the
following two ways: -
* A neural network acquires knowledge through
learning.
*A neural network’s knowledge is stored within the
interconnection strengths known as synaptic
weight.

6. ARTIFICIAL NEURON MODEL
 Inputs to the network are
represented by the mathematical
symbol, xn
 Each of these inputs are
multiplied by a connection
weight , wn
sum = w1 x1 + ……+ wnxn
 These products are simply
summed, fed through the transfer
function, f( ) to generate a result
and then output.
f
w1
w2
xn
x2
x1
wn
f(w1 x1 + ……+ wnxn

7. Learning
 In artificial neural networks, learning refers to the method
of modifying the weights of connections between the
nodes of a specified network.
 The learning ability of a neural network is determined by
its architecture and by the algorithmic method chosen for
training.
 They are of two types.

8.  This is learning by doing.
 In this approach no sample
outputs are provided to the
network against which it
can measure its predictive
performance for a given
vector of inputs.
UNSUPERVISED LEARNING
• A teacher is available
• The training data consist of
pairs of input and desired
output values that are
traditionally represented in
data vectors.
SUPERVISED LEARNING

9. Applications
 Character Recognization
 Image Compression
 Stock Market Pridiction
 Medicine, Electronic Nose,
Security, and Loan
Applications

10. Advantages
 It involves human like thinking.
 They handle noisy or missing data.
 They can work with large number of variables or
parameters.
 They provide general solutions with good predictive
accuracy.
 System has got property of continuous learning.

11. Disadvantages
 Needs training to operate
 Architecture of NN is different from the architecture
of microprocessor.Therefore needs to be emulated
 Requires high processing time for large networks