Artificial Neural Networks and Their Applications Prof. Les Sztandera

Artificial Neural Networks Artificial neural networks (ANNs) are programs designed to simulate the way a simple biological nervous system is believed to operate. These networks have the capacity to learn, memorize and create relationships amongst data The most widely used ANN is known as the back propagation ANN. This type of ANN is excellent at prediction and classification tasks Another is the Kohonen or self organizing map which is excellent at finding relationships amongst complex sets of data.

Who Needs ANNs?  People that have to work with or analyze data of any kind.  People in business, finance, industry, education and science whose problems are complex, laborious, fuzzy or simply un- resolvable using present methods.  People who want better solutions and wish to gain a competitive edge.

Why Are ANNs Better? 1. They deal with the non-linearity in the world in which we live. 2. They handle noisy or missing data. 3. They create their own relationship amongst information - no equations! 4. They can work with large numbers of variables or parameters. 5. They provide general solutions with good predictive accuracy.

What Are ANNs Used For ? Their applications are almost limitless, but fall into a few simple categories  Classification  Forecasting  Modeling

Classification Customer/market profiles, medical diagnosis, signature verification, loan risk evaluation, voice recognition, image recognition, spectra identification, property valuation, classification of cell types, microbes, materials, samples.

Forecasting Future sales, production requirements, market performance, economic indicators, energy requirements, medical outcomes, chemical reaction products, weather, crop forecasts, environmental risk, horse races, jury panels.

Modeling Process control, systems control, chemical structures, dynamic systems, signal compression, plastics molding, welding control, robot control, and many more.

Neural Networks at work – an example  This simulation will take you inside a neural network, so you can get a good overview of how neural networks are constructed internally