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Neural Networks Dr. Peter Phillips

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The Human Brain (Recap of week 1)

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A Classic Artificial Neuron (Recap cont.) X1X1 X2X2 X3X3 SjSj Output W1W1 W2W2 W3W3 f(S j )

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Unsupervised Learning Today’s lecture will consider the use of Self Organising Map (SOM) and Unsupervised Learning Recall that Supervised Learning matches inputs to outputs. Unsupervised Learning Classifies the data into classes

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The Biological Basis for Unsupervised Neural Networks Major sensory and motor systems are ‘topographically mapped’ in the brain –Vision: retinotopic map –Hearing: tonotopic map –Touch: somatotopic map

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Kohonen Self-Organising Maps The most famous unsupervised learning network is the Kohonen Network. Neural network algorithm using unsupervised competitive learning Primarily used for organization and visualization of complex data Teuvo Kohonen

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Understanding the Data Set A good understanding of the data set is essential to use a SOM – or any network for that matter A ‘distance measure’ and/or suitable rescaling must be defined to allow meaningful comparison The data must be of good quality and must be representative of the application area

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SOM - Architecture 2d array of neurons Set of input signals (connected to all neurons in lattice) Weighted synapses x1x1 x2x2 x3x3 xnxn... w j1 w j2 w j3 w jn j

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Finding a Winner (2) Euclidean distance between two vectors a and b, a = (a 1,a 2,…,a n ), b = (b 1,b 2,…b n ), is calculated as: i.e. Pythagoras’ Theorem Other distance measures could be used, e.g. Manhattan distance Euclidean distance

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SOM Parameters The learning rate and neighbourhood function define to what extent the weights of each node are adjusted

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Neighbourhood function Degree of neighbourhood Distance from winner Degree of neighbourhood Distance from winner Time

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Data For Tutorial Work Data collected from a UHT plant at Leatherhead Consists of 300 cases use 150 for Training and 150 for Testing Data collected with plant running in normal state, during cleaning of exchangers and with fault

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Tutorial 2 (UHT Plant Data)

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My settings First run 70 epochs – learning rate 0.6 to 0.1 Second run 50 epochs – learning rate constant 0.1 First run Neighbourhood kept to 1 Second run Neighbourhood start 1 end 0

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Trajan Classification

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SOM – New Data A trained SOM can be used to classify new input data The input data is classified to the node with the ‘best’ or ‘closest’ weights Previous knowledge of other data samples assigned to the same class enable inferences to be made about the ‘new’ input sample

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