by Christos Stergiou and Dimitrios Siganos | |
This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in medicine are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated.
1. Introduction to Neural Networks
1.1 What is a neural network?
1.2 Historical background
1.3 Why use neural networks?
1.4 Neural networks versus conventional computers - a comparison
2. Human and Artificial Neurones - investigating the similarities 2.1 How the Human Brain Learns?
2.2 From Human Neurones to Artificial Neurones
3. An Engineering approach
3.1 A simple neuron - description of a simple neuron
3.2 Firing rules - How neurones make decisions
3.3 Pattern recognition - an example
3.4 A more complicated neuron
4. Architecture of neural networks
4.1 Feed-forward (associative) networks
4.2 Feedback (autoassociative) networks
4.3 Network layers
5. The Learning Process
5.1 Transfer Function
5.2 An Example to illustrate the above teaching procedure 5.3 The Back-Propagation Algorithm
6. Applications of neural networks
6.1 Neural networks in practice
6.2 Neural networks in medicine
6.2.1 Modelling and Diagnosing the Cardiovascular System 6.2.2 Electronic noses - detection and reconstruction of odours by ANNs 6.2.3 Instant Physician - a commercial neural net diagnostic program 6.3 Neural networks in business
6.3.2 Credit evaluation
Appendix A - Historical background in detail
Appendix B - The back propogation algorithm - mathematical approach Appendix C - References used throughout the review
1. Introduction to neural networks
1.1 What is a Neural Network?
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.
1.2 Historical background
Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras. Many importand advances have been boosted by the use of inexpensive computer emulations. Following an initial period of enthusiasm, the field survived a period of frustration and disrepute. During this period when funding and professional support was minimal, important advances were made by relatively few reserchers. These pioneers were able to develop convincing technology which surpassed the limitations identified by Minsky and Papert. Minsky and Papert, published a book (in 1969) in which they summed up a general feeling of frustration (against neural networks) among researchers, and was thus accepted by most without further analysis. Currently, the neural network field enjoys a resurgence of interest and a corresponding increase in funding. For a more detailed description of the history click here
The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch...
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