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A Tribute To

Mr.Frank Rosenblatt

Father of Artificial Neuron Networking

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SEMINAR ONARTIFICIAL NEURAL NETWORKING

By

Arkaj yoti Bhattacharjee

Under The Guidance Of :-

Mr.Manas Ranjan Nayak

Roll-10,Regd no-0505247099

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INTRODUCTION

There is no known algorithm for predicting solventaccessibilit y or coordination number.

Man y different approaches were tried, and most of them utilized the concept of neural networks .

We shall discuss what these networks are, how dothey work, and how we use them for our cause.

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ARTIFICIAL NEURAL

NETWORK Attempts to mimic the actions of the neuralnetworks of the human bod y

Lets first look at how a biological neuralnetwork works A neuron is a single cell that conducts a

chemicall y-based electronic signal At an y point in time a neuron is in either an

excited or inhibited state

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S TRUCTURE OF A NEURON

A series of connected neurons forms a pathwa y A series of excited neurons creates a strong

pathwa y A biological neuron has multiple input tentacles

called dendrites and one primar y output tentaclecalled an axon

The gap between an axon and a dendrite is called asynapse

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NEURAL NETWORKINGIN A BIOLOGICAL CELL

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ARTIFICIAL NEURAL

NETWORK S

Each processing element in an artificial neuralnet is analogous to a biological neuron

An element accepts a certain number of inputvalues and produces a single output value of either 0 or 1

Associated with each input value is a numeric

weight

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FEATURE S OF ANN

NNs attempt to model the wa y the brain is structured:

10 billion neurons that communicate via 60 trillionconnections (s ynapses).

Parallel rather than sequential processing.

NNs are composed of the following elements:

Neuron (soma) Inputs (dendrites)

Outputs of Neurons (axons)

Weights (s ynapse)

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THE ACTIVITIE S WITHIN A

PROCESS

ING UNIT

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HOW ANN WORK?

In the preceding figure, all of the zero th inputs to either thehidden our output la yer are referred to as thresholds and arety picall y set to -1.

The weights of a neural network can be an y positive or negative value.

The input values are multiplied b y the weights that connect

them to a particular neuron.Neurons take this weighted sum as input and use an

activation function to compute the neurons output.

The output of one neuron becomes the input to another neuron multiplied b y a different subset of weights.

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TYPE S OF NETWORK

Multilayer Perceptron

Radial Basis Function

Kohonen

Linear

Hopfield

Adaline/Madaline

Probabilistic Neural Network (PNN)General Regression Neural Network (GRNN)

and at least thirty others

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NEURAL NETWORK S US ES

S peech recognition S peech synthesis Im age recognition

Pattern recognition S tock m arket predictionRobot control and navigation

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Strengths of Artificial Neural NetworksNeural NetworksAre Versatile

Neural Networks Are Versatile Neural Networks Can Produce Good

Results in Complicated Domains

Neural Networks Can HandleCategorical and Continuous Data T y pes

Neural Networks Are Available inMan y Off-the- S helf Packages

S TRENGTH S OF NEURAL NETWORKING

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All Inputs and Outputs Must BeMassaged to

Neural Networks Cannot Explain

Results Neural Networks Ma y Converge on

an Inferior S olution

WEAKNE SS ES OF ARTIFICIAL

NEURAL NETWORK S

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CONCLUSION Neural network are ver y flexible and powerful.

If used sensibl y they can produce some amazingresults.

It has a ver y vast scope in this modern world.

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REFERENCE S

B . YEGNANARAYANA, S ur yakanth V. Gangashett y, andS . Palanivel, Autoassociative Neural Network Models for Pattern Recognition Tasks in S peech and Image, in AshishGhosh and S ankar K. Pal (Eds.), S oft ComputingApproach to Pattern Recognition and Image Processing,World S cientific Publishing Co. Pte. Ltd., S ingapore,2002.

B . YEGNANARAYANA and C. Chandra S ekhar, PatternRecognition Issues in S peech Processing in S ankar. K. Paland Amita Pal (Eds.), Pattern Recognition from Classicalto Modern Approaches, World S cientific, S ingapore, 2001.

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