Neural Networks

Abstract

The power and speed of modern digital computers is truly astounding. No human can ever hope to compute a million operations a second. However, there are some tasks for which even the most powerful computers cannot compete with the human brain, perhaps not even with the intelligence of an earthworm. Imagine the power of the machine which has the abilities of both computers and humans. It would be the most remarkable thing ever. And all humans can live happily ever after (or will they?). Before discussing the specifics of artificial neural nets though, let us examine what makes real neural nets - brains - function the way they do. Perhaps the single most important concept in neural net research is the idea of connection strength.

Refer:
Neural-Networks Report
Neural-Networks-Ppt
neural-networks-ppt
Seminar Report on neural network and their applications

Neural Networks  [ppt]

Artificial Neural Networks (ANNs) are biologically inspired. Specifically, they borrow ideas from the manner in which the human brain works. The human brain is composed of special cells called neurons.  Estimates of the number of neurons in a human brain cover a wide range (up to 150 billion), and there are more than a hundred different kinds of neurons, separated into groups called networks. Each network contains several thousand neurons that are highly interconnected. Thus, the brain can be viewed as a collection of neural networks

 

Today’s ANNs, whose application is referred to as neural computing, use a very limited set of concepts from biological neural systems. The goal is to simulate massive parallel processes that involve processing elements interconnected in a network architecture. The artificial neuron receives inputs analogous to the electrochemical impulses biological neurons receive from other neurons. The output of the artificial neuron corresponds to signals sent out from a biological neuron. These artificial signal can be changed, like the signals from the human brain. Neurons in an ANN receive information from other neurons or from external source, transform or process the information, and pass it on to other neurons or as external outputs.