a-nn - Artificial Neural Networks Outline What are Neural...

Info iconThis preview shows pages 1–15. Sign up to view the full content.

View Full Document Right Arrow Icon
Artificial Neural Networks
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2 Outline What are Neural Networks? Biological Neural Networks ANN – The basics Feed forward net Training Example – Voice recognition Applications – Feed forward nets Recurrency Elman nets Hopfield nets Central Pattern Generators Conclusion
Background image of page 2
3 What are Neural Networks? Models of the brain and nervous system Highly parallel Process information much more like the brain than a serial computer Learning Very simple principles Very complex behaviors Applications As powerful problem solvers As biological models
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
4 Biological Neural Nets Pigeons as art experts (Watanabe et al. 1995) Experiment: • Pigeon in Skinner box Present paintings of two different artists (e.g. Chagall / Van Gogh) • Reward for pecking when presented a particular artist (e.g. Van Gogh)
Background image of page 4
5
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
6
Background image of page 6
7
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
8 Pigeons were able to discriminate between Van Gogh and Chagall with 95% accuracy (when presented with pictures they had been trained on) Discrimination still 85% successful for previously unseen paintings of the artists Pigeons do not simply memorise the pictures They can extract and recognise patterns (the ‘style’) They generalise from the already seen to make predictions This is what neural networks (biological and artificial) are good at (unlike conventional computer)
Background image of page 8
9 ANNs – The basics ANNs incorporate the two fundamental components of biological neural nets: 1. Neurones (nodes) 2. Synapses (weights)
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
10 Neurone vs. Node
Background image of page 10
11 Structure of a node: Squashing function limits node output:
Background image of page 11

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
12 Synapse vs. weight
Background image of page 12
13 Feed-forward nets Information flow is unidirectional • Data is presented to Input layer • Passed on to Hidden Layer • Passed on to Output layer Information is distributed • Information processing is parallel Internal representation (interpretation) of data
Background image of page 13

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Feeding data through the net: (1 × 0.25) + (0.5 × (-1.5)) = 0.25 + (-0.75) = - 0.5 0.3775 1 1 5 . 0 = + e
Background image of page 14
Image of page 15
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 01/20/2011 for the course CS 6810 taught by Professor Hecker during the Spring '10 term at CSU East Bay.

Page1 / 45

a-nn - Artificial Neural Networks Outline What are Neural...

This preview shows document pages 1 - 15. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online