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Artificial Neural Networks (ANN’s)
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  1. Artificial Neural Networks (ANN’s) Jacob Drilling & Justin Brown

  2. What is an Artificial Neural Network? • A computational model inspired by animals’ central nervous systems. • Composed of connected processing nodes (neurons). • They are capable of machine learning and are exceptional in pattern recognition. • A Network is application specific.

  3. History •Warren McCulloch and Walter Pitts • Threshold Logic •Frank Rosenblatt • Perceptron •Marvin Minsky and Seymour Papert • The Society of Mind Theory •Paul Werbos • Backpropagation •David E. Rumelhart and James McClelland

  4. Biological Neural Networks • A human neuron has three parts: the cell body, the axon and dendrites. • The process of sending a signal...

  5. Artificial Networks • The Artificial model is comprised of many processing nodes (neurons). • Nodes are highly connected with weighted paths. • It has 3 layers: • Input • Hidden • Output

  6. Artificial Networks • Each node does its own processing. • Nodes output according to their activation function. • Initial weights are random. • Back Propagation Algorithm “teaches” by changing weights.

  7. Types • Function • Feed Forward • Feed Back • Structure • Bottleneck • Deep learning

  8. Current Uses • Recognition • Image • Speech • Pattern • Character • Compression • Image • Audio/Video • ALVINN - Driverless car

  9. Feed Forward Algorithm • Input -> Output • Each neuron must sum the weighted products from the previous layer. • Output using activation function.

  10. Back Propagation • Output -> Input • Training Algorithm • Calculates Error in the output layer • Propagates Error backwards to change weights

  11. Criticism/Negative Aspects • Large amounts of computing power and storage are needed • Cost efficiency • Human abilities • Instinct • Logic

  12. Character Recognition • Image Processing 1 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 0 0 0 0 1 1 1 10

  13. Character Recognition 2. Input data in the ANN N = 15 1 N = 10 . 1 . . 0 0 . . . . 100 . 1 1

  14. Character Recognition 2. Input data in the ANN N = 15 1 N = 10 . 1 . . 0 0 . . . . 100 . 1 1