This document provides an overview of artificial neural networks. It discusses how ANNs are inspired by biological neural systems and composed of interconnected processing elements called neurons. ANNs are configured through a learning process to perform tasks like pattern recognition or data classification. The document outlines the basic components of ANNs, including different types of network architectures like feedforward and feedback networks. It provides examples of applications for ANNs, such as speech and image recognition. In conclusion, it discusses using ANNs for applications in fields like medicine and business.

1. By:
P.Joshna rani
16013D7902

2.  Introduction
 Human and artificial neurons
 Engineering approach
 Architectural neural networks
 Advantages
 Applications
 Conclusion

3.  An Neural Network (NN) is an information processing
paradigm that is inspired by the way biological nervous
systems, such as the brain, process information.
 It is composed of a large number of highly
interconnected processing elements (neurons) working in
unison to solve specific problems.
 An ANN is configured for a specific application, such
as pattern recognition or data classification, through a
learning process.

4.  In human brain a typical neuron collects signals from others
through a host of structures called dendrites.the neuron
sends out the spikes of the electrical activity through a
thin,long stand called axon,which splits into thousands of
branches.
 When a neuron receives excitatory input that is sufficiently
large compared with its inhibitory input, it sends a spike of
electrical activity down its axon.

6.  An artificial neuron is a device with many inputs and one
output. The neuron has two modes of operation; the training
mode and the using mode. In the training mode, the neuron
can be trained to fire (or not), for particular input patterns.
 In the using mode, when a taught input pattern is detected at
the input, its associated output becomes the current output.

7. A simple neuron

8. Feed-forward networks
Feed-forward ANNs allow signals to travel one way only; from input
to output. There is no feedback (loops) i.e. the output of any layer
does not affect that same layer. Feed-forward ANNs tend to be
straight forward networks that associate inputs with outputs. They
are extensively used in pattern recognition. This type of
organisation is also referred to as bottom-up or top-down.
Feedback networks
Feedback networks can have signals travelling in both directions by
introducing loops in the network. Feedback networks are very

9. introducing loops in the network. Feedback networks are very
powerful and can get extremely complicated.
 Feedback networks are dynamic; their 'state' is changing
continuously until they reach an equilibrium point. They remain at
the equilibrium point until the input changes and a new
equilibrium needs to be found.
 Feedback architectures are also referred to as interactive or
recurrent, although the latter term is often used to denote feedback
connections in single-layer organisations.

10. example

11. The common type of artificial neural network
consists of three groups, or layers, of units: a layer of
"input" units is connected to a layer of "hidden" units,
which is connected to a layer of "output" units.

12. Adaptive learning: An ability to learn how to do tasks based on the
data given for training or initial experience.
Self-Organisation: An ANN can create its own organisation or
representation of the information it receives during learning time.
Real Time Operation: ANN computations may be carried out in
parallel, and special hardware devices are being designed and
manufactured which take advantage of this capability.
Fault Tolerance via Redundant Information Coding: Partial
destruction of a network leads to the corresponding degradation of
performance.

13. Neural Networks in Practice:
ANN are also used in the following specific paradigms:
recognition of speakers in communications; diagnosis
of hepatitis; recovery of telecommunications from
faulty software;
interpretation of multimeaning Chinese words; undersea
mine detection; texture analysis; three-dimensional
object recognition; hand-written word recognition; and
facial recognition.

14.  Modelling and Diagnosing the Cardiovascular System
 Electronic noses
 Instant Physician
 Neural Networks in business
 Neural networks in medicine

15.  A Neural Network can be successfully used in the
C.A.I.S. project, given that we have access to enough
training data.
 Since we need it to be highly accurate, we need to
carefully gather contextual variables that closely relate
to diagnosing heart murmur and its various grades.

16.  Neural network design – Hagan
 Neural networks for pattern recognition-Bishop