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Introduction to Neural Network

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).

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Introduction to Neural Network

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  1. Introduction to Neural Network Justin Jansen December 9th 2002

  2. 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

  3. 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

  4. Single Neuron Structure of a neuron in a neural net

  5. Three Layers Neural Net Neural net with three neuron layers

  6. Control Systems Application

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

  8. Limits to Neural Networks • 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

  9. The Advantage Using Neural Network • 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.

  10. Three Main Applications • 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.

  11. Example – Pattern Recognition • Neural Network Best Fit Curve

  12. Questions?

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