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Artificial Intelligence Techniques

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Aims: Section fundamental theory and practical applications of artificial neural networks.

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Aims: Session Aim Introduction to the biological background and implementation issues relevant to the development of practical systems.

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Biological neuron Taken from http://hepunx.rl.ac.uk/~candreop/minos/NeuralN ets/neuralNetIntro.html

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Human brain consists of approx. 10 billion neurons interconnected with about 10 trillion synapses.

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A neuron: specialized cell for receiving, processing and transmitting informations.

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Electric charge from neighboring neurons reaches the neuron and they add.

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The summed signal is passed to the soma that processing this information.

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A signal threshold is applied.

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If the summed signal > threshold, the neuron fires

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Constant output signal is transmitted to other neurons.

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The strength and polarity of the output depends features of each synapse

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varies these features - adapt the network.

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varies the input contribute - vary the system!

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Simplified neuron Taken from http://www.geog.leeds.ac.uk/people/a.turner/pr ojects/medalus3/Task1.htm

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Exercise 1 In groups of 2-3, as a group: Write down one question about this topic?

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McCulloch-Pitts model X1 X2 X3 W1 W2 W3 T Y Y=1 if W1X1+W2X2+W3X3 T Y=0 if W1X1+W2X2+W3X3

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McCulloch-Pitts model Y=1 if W1X1+W2X2+W3X3 T Y=0 if W1X1+W2X2+W3X3

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Logic functions - OR X1 X2 1 1 1 Y Y = X1 OR X2

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Logic functions - AND X1 X2 1 1 2 Y Y = X1 AND X2

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Logic functions - NOT X 0 Y Y = NOT X

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McCulloch-Pitts model X1 X2 X3 W1 W2 W3 T Y Y=1 if W1X1+W2X2+W3X3 T Y=0 if W1X1+W2X2+W3X3

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Introduce the bias Take the threshold over to the other side of the equation and replace it with a weight W0 which equals -T, and include a constant input X0 which equals 1.

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Introduce the bias Y=1 if W1X1+W2X2+W3X3 - T 0 Y=0 if W1X1+W2X2+W3X3 -T <0

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Introduce the bias Lets just use weights – replace T with a ‘fake’ input ‘fake’ is always 1.

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Introduce the bias Y=1 if W1X1+W2X2+W3X3 +W0X0 0 Y=0 if W1X1+W2X2+W3X3 +W0X0 <0

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Short-hand notation Instead of writing all the terms in the summation, replace with a Greek sigma Σ Y=1 if W1X1+W2X2+W3X3 +W0X0 0 Y=0 if W1X1+W2X2+W3X3 +W0X0 <0 becomes

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Logic functions - OR X1 X2 1 1 Y Y = X1 OR X2 X0

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Logic functions - AND X1 X2 1 1 Y Y = X1 AND X2 X0 -2

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Logic functions - NOT X1 Y Y = NOT X1 X0 0

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The weighted sum The weighted sum, Σ WiXi is called the “net” sum. Net = Σ WiXi y=1 if net 0 y=0 if net < 0

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Multi-layered perceptron Feedback network Train by passing error backwards Input-hidden-output layers Most common

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Multi-layered perceptron (Taken from Picton 2004) Input layer Hidden layer Output layer

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Hopfield network Feedback network Easy to train Single layer of neurons Neurons fire in a random sequence

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Hopfield network x1 x2 x3

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Radial basis function network Feedforward network Has 3 layers Hidden layer uses statistical clustering techniques to train Good at pattern recognition

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Radial basis function networks Input layer Hidden layer Output layer

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Kohonen network All neurons connected to inputs not connected to each other Often uses a MLP as an output layer Neurons are self-organising Trained using “winner-takes all”

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What can they do? Perform tasks that conventional software cannot do For example, reading text, understanding speech, recognising faces

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Neural network approach Set up examples of numerals Train a network Done, in a matter of seconds

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Learning and generalising Neural networks can do this easily because they have the ability to learn and to generalise from examples

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Learning and generalising Learning is achieved by adjusting the weights Generalisation is achieved because similar patterns will produce an output

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Summary Neural networks have a long history but are now a major part of computer systems

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Summary They can perform tasks (not perfectly) that conventional software finds difficult

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Introduced McCulloch-Pitts model and logic Multi-layer preceptrons Hopfield network Kohenen network

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Neural networks can Classify Learn and generalise.

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