pattern recognition using neural networks
Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
Active In SP

Posts: 1
Joined: Jul 2010
19-07-2010, 06:45 PM

agud evng sir/msdam. Iam pavithra studying M.Tech production engineering. I wanna do a seminar and presentation on PATTERN RECOGNITION USING NEURAL NETWORKS.. I dont know how to relate neural network to pattern recognition and also how to code the network for the pattern we want to recognise.
Can i expect complete information on this topic? so, please send me the file ..
Thanking u[/size]
Active In SP

Posts: 27
Joined: Jul 2010
19-07-2010, 07:38 PM

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.

Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide project and implimentationions given new situations of interest and answer "what if" questions.
Other advantages include:

1. Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
2. Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.
3. 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.
4. Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

Neural networks take a different approach to problem solving than that of conventional computers. Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order to solve a problem. Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem. That restricts the problem solving capability of conventional computers to problems that we already understand and know how to solve. But computers would be so much more useful if they could do things that we don't exactly know how to do.

Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements(neurones) working in parallel to solve a specific problem. Neural networks learn by example. They cannot be programmed to perform a specific task. The examples must be selected carefully otherwise useful time is wasted or even worse the network might be functioning incorrectly. The disadvantage is that because the network finds out how to solve the problem by itself, its operation can be unpredictable.

image processing is the field of resarch concerned with the development of computer algorithm for digitised image. Image processing problems are solved by a chain of tasks. for image processing problems with linear sollutions, there are good justifiable solutions available. However for solving non linear models, many linear approximations has to made and there occurs very high computational complexities. Many algorithms become intracable when non linearity has to be introduced. problems such as image understanding and object detection cannot be solved properly using standard algorithms. All these leads to the idea for a non linear algorithm that can be trained rather can be designed. this is where artificial neural network concept comes handy.

use the following links for more details

.pdf   aiep_02.pdf (Size: 3.89 MB / Downloads: 144)
Active In SP

Posts: 1
Joined: Oct 2010
15-10-2010, 08:59 PM

hi i am a final year engineering student, so i need a complete report of face recognize using neural network pdf file. please send me .
Active In SP

Posts: 1
Joined: Nov 2010
17-11-2010, 09:51 PM

I'm new to this filed. Please give me some advice to start. Where I can a hardware device to collect pattern. How to create a neural network...
computer science crazy
Super Moderator

Posts: 3,048
Joined: Dec 2008
18-11-2010, 01:14 PM

learn these academic resource to learn How to create a neural network

i this resource is useful ..
Use Search at wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion
sarnendu paul
Active In SP

Posts: 1
Joined: Apr 2012
08-04-2012, 10:58 AM


firstly, u hv to define both the terms.

after that take any example such as-its application in medical field

it is available in google.

Important Note..!

If you are not satisfied with above reply ,..Please


So that we will collect data for you and will made reply to the request....OR try below "QUICK REPLY" box to add a reply to this page

Quick Reply
Type your reply to this message here.

Image Verification
Please enter the text contained within the image into the text box below it. This process is used to prevent automated spam bots.
Image Verification
(case insensitive)

Possibly Related Threads...
Thread Author Replies Views Last Post
Question Optimal Stochastic Location Updates in Mobile Ad Hoc Networks Guest 5 1,542 01-05-2014, 09:10 AM
Last Post: Guest
Last Post: seminar project topic
  gesture recognition technlogy Guest 2 798 14-08-2013, 03:24 PM
Last Post: study tips
  Face recognition technology joshmajoseph 4 2,337 24-07-2013, 09:28 AM
Last Post: study tips
  Link Positions Matter: A Non-Commutative Routing Metric for Wireless Mesh Networks Guest 0 438 27-06-2013, 09:03 PM
Last Post: Guest
  An Adaptive Opportunistic Routing for Wireless Ad-hoc Networks Guest 1 877 20-05-2013, 09:38 AM
Last Post: study tips
  fast data collection in tree based wireless sensor networks vodafonehutch 1 757 18-04-2013, 09:41 AM
Last Post: study tips
  Towards Reliable Data Delivery for Highly Dynamic Mobile Ad Hoc Networks Guest 0 455 05-04-2013, 10:56 AM
Last Post: Guest
  on optimizing overlay topologies for struted in unstruted peer to peer networks docum 0 468 09-03-2013, 05:49 PM
Last Post:
  bluetooth based smart sensor networks 5 2,411 05-03-2013, 09:59 AM
Last Post: seminar tips