(Approved by AICTE & affiliated to UPTU, Lucknow) A Seminar Report On Artificial Neural Network Submitted in partial fulfillment of the requirement for the award of the degree of B.Tech in Information Technology DEPARTMENT OF COMPUTER SCIENCE & INFORMATION TECHNOLOGY ENGG. Submitted By:- ANJALI Branch- IT Semester- 6 th Dr. Anand Sharma Mr. Konark Sharma (HOD, CS/IT Dept.) (Seminar-in-Charge) 2015-2016
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(Approved by AICTE & affiliated to UPTU, Lucknow)
A
Seminar Report On
Artificial Neural Network
Submitted in partial fulfillment of the requirement for the award of
the degree of
B.Tech in
Information Technology
DEPARTMENT OF COMPUTER SCIENCE &
INFORMATION TECHNOLOGY ENGG.
Submitted By:-
ANJALI
Branch- IT
Semester- 6th
Dr. Anand Sharma Mr. Konark Sharma
(HOD, CS/IT Dept.) (Seminar-in-Charge)
2015-2016
2
CERTIFICATE
This is to certify that the Seminar Report entitled “Artificial Neural Network”
submitted by Ms. ANJALI has been a record of student’s own work carried out
individually in my guidance for the partial fulfillment of the degree Of Bachelor
Of Technology in Information Technology of Aligarh College Of Engineering &
Technology during the 6th Sem.
It is further certified to the best of my knowledge and belief that this work has
not been submitted elsewhere for the award of any other degree.
___________________
Mr. Konark Sharma
(Seminar In-charge)
3
ACKNOWLEDGEMENT
All praise to Almighty, the most beneficent, the most merciful, who bestowed
upon us the courage, patience and strength to embark upon this work and carry
it to the completion.
I feel privileged to express my deep sense of gratitude and highest appreciation
to
Mr. Konark Sharma,
Asst. professor,
Dept. of CS/IT Engg.
for his instant support and providing me with incalculable suggestions and
guidance. I sincerely acknowledge him for his support on literature, critical
comments & moral support which he rendered at all stages of the discussion
which was deeply helpful.
I also acknowledge my friends & Parents for their moral support & timely ideas
in completion of this Seminar. I promise to pay the reward of their help &
guidance in form of similar or even better ways to support others throughout
my life.
___________________
Anjali
4
1) Introduction 5-6
2) ANN’s Basic Structure 7-8
3) Types of ANNs 9-10
4) Machine Learning 11
5) Comparisons 12
6) Properties of ANNs 13
7) Applications of ANNs 14
8) Advantages 15
9) Disadvantages 15
10) Conclusion 16
11) References 16
INDEX
5
In machine learning and cognitive science, artificial neural networks (ANNs)
are a family of models inspired by biological neural networks (the central nervous
systems of animals, in particular the brain) and are used to estimate or
approximate functions that can depend on a large number of inputs and are
generally unknown. Artificial neural networks are generally presented as systems
of interconnected "neurons" which exchange messages between each other. The
connections have numeric weights that can be tuned based on experience, making
neural nets adaptive to inputs and capable of learning.
For example, a neural network for handwriting recognition is defined by a set of
input neurons which may be activated by the pixels of an input image. After being
weighted and transformed by a function (determined by the network's designer),
the activations of these neurons are then passed on to other neurons. This process
is repeated until finally, an output neuron is activated. This determines which
character was read.
Like other machine learning methods – systems that learn from data – neural
networks have been used to solve a wide variety of tasks that are hard to solve
using ordinary rule-based programming, including computer vision and speech
recognition.
Background
Examinations of humans' central nervous systems inspired the concept of
artificial neural networks. In an artificial neural network, simple artificial nodes,
known as "neurons", "neurodes", "processing elements" or "units", are connected
together to form a network which mimics a biological neural network.
There is no single formal definition of what an artificial neural network is.
However, a class of statistical models may commonly be called "neural" if it
possesses the following characteristics:
1. Contains sets of adaptive weights, i.e. numerical parameters that are tuned
by a learning algorithm, and
2. Capability of approximating non-linear functions of their inputs.
The adaptive weights can be thought of as connection strengths between neurons,
which are activated during training and prediction.
Neural networks are similar to biological neural networks in the performing of
functions collectively and in parallel by the units, rather than there being a clear
delineation of subtasks to which individual units are assigned. The term "neural
network" usually refers to models employed in statistics, cognitive psychology
and artificial intelligence. Neural network models which command the central