1. Artificial Neural Systems

2. Intro Artificial neural systems try to process information in the same way as the human brain does. Traditional computer systems process data using the "Von Neuman" model. Artificial neural networks (ANNs) try to imitate the way the human brain is organised and the way the brain handles information.

3. Traditional (von neuman) computer system A human written program tells the computer system how to use inputs and follow a plan to produce appropriate output. The human programmer uses complex theories to make a sophisticated plan that will make the computer system successful. The instructions in the program are carried out one after the other at a rate of hundreds of millions of instructions per second (MIPS) by a single complex and powerful processor. This is NOT how a human brain is physically structured nor is it the method used by the brain to process information.

4. Human brain and neurons A human brain has about 200,000 neurons. A neuron is the type of brain cell most associated with intelligent behaviour. A neuron is quite a simple "device" but each neuron has connections to many many other neurons. The pattern of interconnection is very complex. Each neuron receives signals from other neurons which it may,(or may not), pass on to other neurons. The brain processes information by creating complex patterns of signals (neural pathways) being "fired" around large groups of neurons.

5. Human brain and neurons (cont…) Any set of signals may be passed at the same time as other signals are being passed and so the brain operates with parallel processing, in fact very many process may take place at the same time. This parallel activity helps the brain to be fast.

6. ‍Artificial Neural Network (ANN) The ANN tries to imitate the neurons in the human brain. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem. It imitates the brain.

7. There are several layers of neurons. The ANN has an input layer, an output layer and one or more hidden layers in between. Data is entered at the input layer and signals are passed through the connections between layers, though some signals are stronger than others. Each neuron uses a calculation based on its input signal to produce the signal it passes on to the next layer. The output layer delivers the final results.

8. There is no program! The ANN learns how to be successful by training. In the first training session, the ANN takes a completely random guess at the answer (nonsense), getting it wildly wrong. Each training session involves the ANN being given another problem and the ANN output is compared against the correct answer. The difference between the ANN result and the correct answer (error) is fed back through the ANN.

9. The ANN uses the error feedback to alter the pathways between the layers, some pathways are made stronger, others weaker. The pathways become tuned to the correct answer. After the ANN has been trained, it will be used to process new unseen problems of the same type. Usually the ANN will have a very high success rate at solving the problem. The ANN has learned its own way for solving the problem.

10. Applications of ANNs Neural networks are best at identifying patterns or trends in data (pattern matching), so they are well suited to prediction or forecasting needs including: Hand-written word recognition (used for reading postcodes) –Have a look some work done at University of Technology in Sydney AustraliaUniversity of Technology in Sydney Australia Stock market prediction; will the shares rise or fall, when should investors buy or sell? –Tradescision produces ANN for market analysis, have a look at their websitewebsite

11. Debt risk assessment; should the bank customer get a loan or not, what are the chances of not getting (all) the money back? Recognition of speakers (voices) in communications; Diagnosis of hepatitis (a liver disease) Three-dimensional object recognition (finger print recognition) Facial recognition (used by modern digital cameras, police forces) –a pattern is some form of sequence or repetition –eg a person's distinctive voice pattern, a fingerprint, the pattern of sales for ice cream over 12 months of the year –ANN is good at any application where there is pattern identification –here is a military use of ANN that did not go as plannedmilitary use of ANN