Introduction to Neural Network Justin Jansen December 9 th 2002

Neural Network Definition of Artificial Neural Network Fundamental concepts Where they fit in a control system What they can do and where they fail Example pattern recognition

What is an Artificial Neural Network? (ANN) A neural network is a computational method inspired by studies of the brain and nervous systems in biological organisms. A common neural network architecture consists of multiple layers of similar elements: –Each unit is called a neuron and is capable of receiving input stimulation). –When the total amount of stimulation received exceeds some predetermined threshold, the neuron "fires" –When highly interconnected, produce dynamic response to inputs

Structure of a neuron in a neural net Single Neuron

Neural net with three neuron layers Three Layers Neural Net

Control Systems Application

· Pattern association · Pattern classification · Regularity detection · Image processing · Speech analysis · Optimization problems · Robot steering · Processing of inaccurate or incomplete inputs · Stock market forecasting · Simulation The areas where neural nets may be useful

The operational problem encountered when attempting to simulate the parallelism of neural networks Instability to explain any results that they obtain Neural networks are, in essence, a "black box" Training time Limits to Neural Networks

Handle partial lack of system understanding Create adaptive models (models that can learn) Noted for their ability to learn patterns in data which is noisy Excellent for situations in which the trainer is unsure of the actual relationships that exist in the training set. The Advantage Using Neural Network

Concurrent simulation, Neural Network results have been validated by the expected real-life behavior. Neural Networks as sub-components of a bigger model to model subsystems that would be hard to model commonly because of a lack of understanding. Adaptive models, "models that can learn", according to an error feedback such model would be able to adapt runtime to situations that hasn't been taken into account. Three Main Applications

Example – Pattern Recognition Neural Network Best Fit Curve

Questions?