Автор: Пользователь скрыл имя, 07 Февраля 2013 в 23:19, аттестационная работа
Neural Networks mimic the pattern of human learning to solve many difficult tasks of data management and pattern recognition. By configuring virtual neural networks that function like the human brain, computers can perform tasks at greater speeds and with increased flexibility of application. These networks are capable of offering invaluable insights into the vast information stockpiles that are common today.
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. Applications of neural networks
2.1 Neural networks in practice
2.2 Neural networks in medicine
2.2.1 Modelling and Diagnosing the Cardiovascular System
2.2.2 Electronic noses - detection and reconstruction of odours by ANNs
2.2.3 Instant Physician - a commercial neural net diagnostic program
2.3 Neural networks in business
2.3.1 Marketing
2.3.2 Credit evaluation
3. Conclusion
The HNC company, founded by Robert Hecht-Nielsen, has developed several neural network applications. One of them is the Credit Scoring system which increase the profitability of the existing model up to 27%. The HNC neural systems were also applied to mortgage screening. A neural network automated mortgage insurance underwritting system was developed by the Nestor Company. This system was trained with 5048 applications of which 2597 were certified. The data related to property and borrower qualifications. In a conservative mode the system agreed on the underwritters on 97% of the cases. In the liberal model the system agreed 84% of the cases. This is system run on an Apollo DN3000 and used 250K memory while processing a case file in approximately 1 sec.
The computing world has a lot to gain fron neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast responseand computational times which are due to their parallel architecture.
Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain.
Perhaps the most exciting aspect of neural networks is the possibility that some day 'consious' networks might be produced. There is a number of scientists arguing that conciousness is a 'mechanical' property and that 'consious' neural networks are a realistic possibility.
Finally, I would like to state that even though neural networks have a huge potential we will only get the best of them when they are intergrated with computing, AI, fuzzy logic and related subjects.