report on neural network
Chapter 1
1. INTRODUCTION
Artificial Neural Networks are being touted as the wave of the future in computing. They are indeed self learning mechanisms which don 't require the traditional skills of a programmer. But unfortunately, misconceptions have arisen. Writers have hyped that these neuron-inspired processors can do almost anything.
Fig. 1.1 Neural Network
These exaggerations have created disappointments for some potential users who have tried, and failed, to solve their problems with neural networks. These application builders have often come to the conclusion that neural nets are complicated and confusing. Unfortunately, that confusion has come from the industry itself. An avalanche of articles have appeared touting a large assortment of different neural networks, all with unique claims and specific examples. Currently, only a few of these neuron-based structures, paradigms actually, are being used commercially. One particular structure, the feedforward, back-propagation network, is by far and away the most popular. Most of the other neural network structures represent models for "thinking" that are still being evolved in the laboratories. Yet, all of these networks are simply tools and as such the only real demand they make is that they require the network architect to learn how to use them.
Chapter 2
2. HISTORY
The study of the human brain is thousands of years old. With the advent of modern electronics, it was only natural to try to harness this thinking process. The first step toward artificial neural networks came in 1943 when Warren McCulloch, a neurophysiologist, and a young mathematician, Walter Pitts, wrote a paper on how neurons might work. They modeled a simple neural network with electrical circuits.
Reinforcing this concept of neurons and how they work was a book written by Donald Hebb. The Organization of Behavior
1. INTRODUCTION
Artificial Neural Networks are being touted as the wave of the future in computing. They are indeed self learning mechanisms which don 't require the traditional skills of a programmer. But unfortunately, misconceptions have arisen. Writers have hyped that these neuron-inspired processors can do almost anything.
Fig. 1.1 Neural Network
These exaggerations have created disappointments for some potential users who have tried, and failed, to solve their problems with neural networks. These application builders have often come to the conclusion that neural nets are complicated and confusing. Unfortunately, that confusion has come from the industry itself. An avalanche of articles have appeared touting a large assortment of different neural networks, all with unique claims and specific examples. Currently, only a few of these neuron-based structures, paradigms actually, are being used commercially. One particular structure, the feedforward, back-propagation network, is by far and away the most popular. Most of the other neural network structures represent models for "thinking" that are still being evolved in the laboratories. Yet, all of these networks are simply tools and as such the only real demand they make is that they require the network architect to learn how to use them.
Chapter 2
2. HISTORY
The study of the human brain is thousands of years old. With the advent of modern electronics, it was only natural to try to harness this thinking process. The first step toward artificial neural networks came in 1943 when Warren McCulloch, a neurophysiologist, and a young mathematician, Walter Pitts, wrote a paper on how neurons might work. They modeled a simple neural network with electrical circuits.
Reinforcing this concept of neurons and how they work was a book written by Donald Hebb. The Organization of Behavior
References: http://howstuffworks.tumblr.com/post/53924031029/futurescope-stanfords-artificial-neural http://www.sciencedirect.com/science/article/pii/S0736584509000441 http://www.google.com/webhp?nord=1#nord=1&q=conclusion+of+artificial+neural+network https://controls.engin.umich.edu/wiki/index.php/NN http://www.learnartificialneuralnetworks.com/introduction-to-neural-networks.html http://www.obitko.com/tutorials/neural-network-prediction/conclusion.html http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html