Artificial Neural Network

Contents

IntoductionArtifcial Neural NetworkBiological Neuron ModelArtificial Neuron ModelApplicationsAdvantagesDisadvantages

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

BIOLOGICAL NEURON MODELFour parts of a typical

nerve cell:-DENDRITES: Accepts

the inputsSOMA:Process the inputsAXON:Turns the

processed inputs into outputs.

SYNAPSES:The electrochemical contact between the neurons

ARTIFICIAL NEURAL NETWORKArtificial 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.

ARTIFICIAL NEURON MODELInputs 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)

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.

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

Applications

Character Recognization

Image Compression

Stock Market Pridiction

Medicine, Electronic

Nose, Security, and Loan

Applications

AdvantagesIt 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.

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