artificial neural networking 1
TRANSCRIPT
-
8/8/2019 Artificial Neural Networking 1
1/18
A Tribute To
Mr.Frank Rosenblatt
Father of Artificial Neuron Networking
-
8/8/2019 Artificial Neural Networking 1
2/18
SEMINAR ONARTIFICIAL NEURAL NETWORKING
By
Arkaj yoti Bhattacharjee
Under The Guidance Of :-
Mr.Manas Ranjan Nayak
Roll-10,Regd no-0505247099
-
8/8/2019 Artificial Neural Networking 1
3/18
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.
-
8/8/2019 Artificial Neural Networking 1
4/18
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
-
8/8/2019 Artificial Neural Networking 1
5/18
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
-
8/8/2019 Artificial Neural Networking 1
6/18
NEURAL NETWORKINGIN A BIOLOGICAL CELL
-
8/8/2019 Artificial Neural Networking 1
7/18
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
-
8/8/2019 Artificial Neural Networking 1
8/18
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)
-
8/8/2019 Artificial Neural Networking 1
9/18
THE ACTIVITIE S WITHIN A
PROCESS
ING UNIT
-
8/8/2019 Artificial Neural Networking 1
10/18
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.
-
8/8/2019 Artificial Neural Networking 1
11/18
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
-
8/8/2019 Artificial Neural Networking 1
12/18
NEURAL NETWORK S US ES
S peech recognition S peech synthesis Im age recognition
Pattern recognition S tock m arket predictionRobot control and navigation
-
8/8/2019 Artificial Neural Networking 1
13/18
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
-
8/8/2019 Artificial Neural Networking 1
14/18
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
-
8/8/2019 Artificial Neural Networking 1
15/18
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.
-
8/8/2019 Artificial Neural Networking 1
16/18
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.
-
8/8/2019 Artificial Neural Networking 1
17/18
-
8/8/2019 Artificial Neural Networking 1
18/18