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**Artificial Neural Networks**

Wismar Business School Artificial Neural Networks Uwe Lämmel

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Literature & Software Robert Callan: The Essence of Neural Networks, Pearson Education, 2002. JavaNNS based on SNNS: Stuttgarter Neuronale Netze Simulator

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Prerequisites NO algorithmic solution available or algorithmic solution too time consuming NO knowledge-based solution LOTS of experience (data) Try a NN

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**Content Idea An artificial Neuron – Neural Network**

Supervised Learning – feed-forward networks Competitive Learning – Self-Organising Map Applications

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**Two different types of knowledge processing**

Logic Conclusion sequential Aware of Symbol processing, Rule processing precise Engineering „traditional" AI Perception, Recognition parallel Not aware of Neural Networks fuzzy Cognitive oriented Connectionism

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**Idea A human being learns by example “learning by doing”**

seeing(Perception), Walking, Speaking,… Can a machine do the same? A human being uses his brain. A brain consists of millions of single cells. A cell is connected with ten thousands of other cell. Is it possible to simulate a similar structure on a computer?

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**Idea Artificial Neural Network**

Information processing similar to processes in a mammal brain heavy parallel systems, able to learn great number of simple cells ? Is it useful to copy nature ? wheel, aeroplane, ...

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Idea An artificial neural network functions in a similar way a natural neural network does. we need: software neurons software connection between neurons software learning algorithms

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**A biological Neuron Dendrits: (Input) Getting other activations**

cell and cell nucleus Axon (Neurit) Dendrits Synapse Dendrits: (Input) Getting other activations Axon: (Output ) forward the activation (from 1mm up to 1m long) Synapse: transfer of activation: To other cells, e.g.. Dendrits of other neurons a cell has about to connections to other cells Cell Nucleus: (processing) evaluation of activation

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**Abstraction Dendrits: weighted connections weight: real number**

Axon: output: real number Synapse: (identity: output is directly forwarded) Cell nucleus: unit contains simple functions input = (many) real numbers processing = evaluation of activation output = real number (~activation)

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**An artificial Neuron w1i w2i wji oi net : input from the network**

... oi net : input from the network w : weight of a connection act : activation fact : activation function : bias/threshold fout : output function (mostly ID) o : output

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**A simple switch a1=__ a2=__ o=__ net= o1w1+o2 w2 a = 1, if net>**

= 0, otherwise o = a w1=__ w2=__ Set parameters according to function: Input neurons 1,2 : a1,a2 input pattern, here: oi=ai weights of edges: w1, w2 bias Give values for w1, w2 , we can evaluate output o

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Questions find values for the parameters so that a logic function is simulated: Logical AND Logical OR Logical exclusive OR (XOR) Identity We want to process more than 2 inputs. Find appropriate parameter values. Logical AND, 3 (4) inputs OR, XOR iff 2 out of 4 are 1

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Mathematics in a Cell Propagation function neti(t) = ojwj = w1i o1 + w2i o Activation ai(t) – Activation at time t Activation function fact : ai(t+1) = fact(ai(t), neti(t), i) i – bias Output function fout : oi = fout(ai)

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**activation functions are sigmoid functions**

Bias function -1,0 -0,5 0,0 0,5 1,0 -4,0 -2,0 2,0 4,0 activation functions are sigmoid functions Identity -4,0 -2,0 0,0 2,0 4,0 -2,5 -1,0 0,5 3,5

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**activation functions are sigmoid functions**

y = tanh(c·x) -1,0 -0,5 0,5 1,0 -0,6 0,6 c=1 c=2 c=3 Logistic function: y = 1/(1+exp(-c·x)) 0,5 1,0 -1,0 0,0 c=1 c=3 c=10 activation functions are sigmoid functions

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**Structure of a network layers input layer – contains input neurons**

output layer – contains output neurons hidden layer – contains hidden neurons An n-layer network has: n layer of connections which can be trained n+1 neuron layers n –1 hidden layers

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**Neural Network - Definition**

A Neural Network is characterized by connections of many (a lot of) simple units (neurons) and units exchanging signals via these connections A neural Network is a coherent, directed graph which has weighted edges and each node (neurons, units ) contains a value (activation).

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**Elements of a NN Connections/Links directed, weighted graph**

weight: wij (from cell i to cell j) weight matrix Propagation function network input of a neuron will be calculated: neti = ojwji Learning algorithm

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Example XOR-Network 1 2 3 1,5 -2 4 0,5 TRUE

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**Supervised Learning – feed-forward networks**

Idea An artificial Neuron – Neural Network Supervised Learning – feed-forward networks Architecture Backpropagation Learning Competitive Learning – Self-Organising Map Applications

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**Multi-layer feed-forward network**

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Feed-Forward Network

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**Evaluation of the net output**

Ni Nj Nk netj netk Oj=actj Ok=actk Training pattern p Oi=pi Input-Layer hidden Layer(s) Output Layer

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**Backpropagation Learning Algorithm**

supervised Learning error is a function of the weights wi : E(W) = E(w1,w2, ... , wn) We are looking for a minimal error minimal error = hollow in the error surface Backpropagation uses the gradient for weight approximation.

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error curve

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**Problem output teaching output hidden layer error in output layer:**

difference output – teaching output error in a hidden layer? input layer

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Mathematics modifying weights according to the gradient of the error function W = - E(W) E(W) is the gradient is a factor, called learning parameter -1 -0,6 -0,2 0,2 0,6 1

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**Mathematics Here: modification of weights: W = – E(W) E(W): Gradient**

Proportion factor for the weight vector W, : learning factor E(Wj) = E(w1j,w2j, ..., wnj)

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**Error Function Modification of a weight: (1)**

Error function quadratic distance between real and teaching output of all patterns p: tj - teaching output oj - real output Now: error for one pattern only (omitting pattern index p): (2)

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**Backpropagation rule Multi layer networks**

Semi linear Activation function (monotone, differentiable, e.g. logistic function) Problem: no teaching outputs for hidden neurons

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**Backpropagation Learning Rule**

Start: (6.1) dependencies: (6.2) fout = Id 6.1 in more detail: (6.3)

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**The 3rd and 2nd Factor 3rd Factor: dependency net input – weights**

(6.4) 2nd Factor: derivation of the activation function: (6.5) (6.7)

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**The 1st Factor 1st Factor: dependency error – output**

Error signal of output neuron j: (6.8) (6.9) Error signal of hidden neuron j: (6.10) j : error signal

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**Error Signal j = f’act(netj)·(tj – oj) j = f’act(netj) · kwjk**

(6.11) (6.12) Output neuron j: j = f’act(netj)·(tj – oj) Hidden neuron j: j = f’act(netj) · kwjk

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**Standard Backpropagation Rule**

For the logistic activation function: f ´act(netj ) = fact(netj )(1 – fact(netj )) = oj (1 –oj) Therefore: and:

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**error signal for fact = tanh**

For the activation function tanh holds: f´act(netj ) = (1 – f ²act(netj )) = (1 – tanh² oj ) therefore:

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**Backpropagation - Problems**

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**Backpropagation-Problems**

A: flat plateau backpropagation goes very slowly finding a minimum takes a lot of time B: Oscillation in a narrow gorge it jumps from one side to the other and back C: leaving a minimum if the modification in one training step is to high, the minimum can be lost

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**Solutions: looking at the values**

change the parameter of the logistic function in order to get other values Modification of weights depends on the output: if oi=0 no modification will take place If we use binary input we probably have a lot of zero-values: Change [0,1] into [-½ , ½] or [-1,1] use another activation function, eg. tanh and use [-1..1] values

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**Solution: Quickprop assumption: error curve is a square function**

calculate the vertex of the curve slope of the error curve:

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**Resilient Propagation (RPROP)**

sign and size of the weight modification are calculated separately: bij(t) – size of modification bij(t-1) + if S(t-1)S(t) > 0 bij(t) = bij(t-1) - if S(t-1)S(t) < bij(t-1) otherwise +>1 : both ascents are equal „big“ step 0<-<1 : ascents are different „smaller“ step -bij(t) if S(t-1)>0 S(t) > 0 wij(t) = bij(t) íf S(t-1)<0 S(t) < 0 -wij(t-1) if S(t-1)S(t) < 0 (*) -sgn(S(t))bij(t) otherwise (*) S(t) is set to 0, S(t):=0 ; at time (t+1) the 4th case will be applied.

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**Limits of the Learning Algorithm**

it is not a model for biological learning we have no teaching output in a natural learning process In a natural neural network there are no feedbacks (at least nobody has discovered yet) training of a artificial neural network is rather time consuming

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**Development of an NN-application**

calculate network output compare to teaching output use Test set data evaluate output change parameters modify weights input of training pattern build a network architecture quality is good enough error is too high

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**Possible Changes Architecture of NN size of a network**

shortcut connection partial connected layers remove/add links receptive areas Find the right parameter values learning parameter size of layers using genetic algorithms

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**Memory Capacity - Experiment**

output-layer is a copy of the input-layer training set consisting of n random pattern error: error = network can store more than n patterns error >> 0 network can not store n patterns memory capacity: error > 0 and error = 0 for n-1 patterns and error >>0 for n+1 patterns

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**Summary Backpropagation is a Backpropagation of Error Algorithm**

works like gradient descent Activation Functions: Logistics, tanh Meaning of Learning parameter Modifications RPROP Backprop Momentum QuickProp Finding an appropriate Architecture: Memory Size of a Network Modifications in layer connection Applications

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**Binary Coding of nominal values I**

no order relation, n-values n neurons, each neuron represents one and only one value: example: red, blue, yellow, white, black 1,0,0,0,0 0,1,0,0,0 0,0,1,0, disadvantage: n neurons necessary, but only one of them is activated lots of zeros in the input

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**Binary Coding of nominal values II**

no order-relation, n values m neurons, of it k neurons switched on for one single value requirement: (m choose k) n example: red, blue, yellow, white, black 1,1,0,0 1,0,1,0 1,0,0,1 0,1,1,0 0,1,0,1 4 neuron, 2 of it switched on, (4 choose 2) > 5 advantage: fewer neurons balanced ratio of 0 and 1

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**Example Credit Scoring**

A1: Credit history A2: debt A3: collateral A4: income network architecture depends on the coding of input and output How can we code values like good, bad, 1, 2, 3, ...?

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**Example Credit Scoring**

class A3: A4:

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**Supervised Learning – feed-forward networks**

Idea An artificial Neuron – Neural Network Supervised Learning – feed-forward networks Competitive Learning – Self-Organising Map Architecture Learning Visualisation Applications

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**Self Organizing Maps (SOM)**

A natural brain can organize itself Now we look at the position of a neuron and its neighbourhood Kohonen Feature Map two layer pattern associator Input layer is fully connected with map-layer Neurons of the map layer are fully connected to each other (virtually)

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**Clustering f ai output B Input set A**

objective: All inputs of a class are mapped onto one and the same neuron f Input set A output B ai Problem: classification in the input space is unknown Network performs a clustering

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Winner Neuron Kohonen- Layer Input-Layer Winner Neuron

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**Learning in an SOM Choose an input k randomly**

Detect the neuron z which has the maximal activity Adapt the weights in the neighbourhood of z: neuron i within a radius r of z. Stop if a certain number of learning steps is finished otherwise decrease learning rate and radius, go on with step 1.

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**A Map Neuron look at a single neuron (without feedback): Activation:**

Output: fout = Id

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Centre of Activation Idea: highly activated neurons push down the activation of neurons in the neighbourhood Problem: Finding the centre of activation: Neuron j with a maximal net-input Neuron j, having a weight vector wj which is similar to the input vector (Euklidian Distance): z: x - wz = minj x - wj

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**Changing Weights z determines the shape of the curve:**

weights to neurons within a radius z will be increased: wj(t+1) = wj(t) + hjz(x(t)-wj(t)) , j z x-input wj(t+1) = wj(t) , otherwise Amount of influence depends on the distance to the centre of activation: (amount of change wj?) Kohonen uses the function : z determines the shape of the curve: z small high + sharp z high wide + flat

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**Changing weights Simulation by a Gauß-curve**

Mexican-Hat-Approach 0,5 1 -3 -2 -1 2 3 Simulation by a Gauß-curve Changing Weights by a learning rate (t), going down to zero Weight change:: wj+1(t+1) = wj(t) + hjz(x(t)-wj(t)) , j z wj+1(t+1) = wj(t) , otherwise Requirements: Pattern input by random! z(t) and z(t) are monotone decreasing functions in t.

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SOM Training Kohonen layer input pattern mp Wj find the winner neuron z for an input pattern p (minimal Euclidian distance) adapt weights of connections winner neuron -input neurons neighbours – input neurons

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**Example Credit Scoring**

A1: Credit History A2: Debts A3: Collateral A4: Income We do not look at the Classification SOM performs a Clustering

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**Credit Scoring good = {5,6,9,10,12} average = {3, 8, 13}**

bad = {1,2,4,7,11,14}

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**Credit Scoring Pascal tool box (1991) 10x10 neurons**

32,000 training steps

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**Visualisation of a SOM Colour reflects Euclidian distance to input**

Weights used as coordinates of a neuron Colour reflects cluster NetDemo ColorDemo TSPDemo

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**Experiment: Pascal Program, 1998**

Example TSP Travelling Salesman Problem A salesman has to visit certain cities and will return to his home. Find an optimal route! problem has exponential complexity: (n-1)! routes 31/32 states in Mexico? Experiment: Pascal Program, 1998

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**Nearest Neighbour: Example**

Kiel Rostock Berlin Hamburg Hannover Frankfurt Essen Schwerin Some cities in Northern Germany: Initial city is Hamburg Exercise: Put in the coordinates of the capitals of all the 31 Mexican States + Mexico/City. Find a solution for the TSP using a SOM!

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**Draw a neuron at position:**

SOM solves TSP Kohonen layer input Draw a neuron at position: (x,y)=(w1i,w2i) w1i= six X w2i= siy Y

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**SOM solves TSP Initialisation of weights:**

weights to input (x,y) are calculated so that all neurons form a circle The initial circle will be expanded to a round trip Solutions for problems of several hundreds of towns are possible Solution may be not optimal!

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**Applications Data Mining - Clustering Customer Data Weblog ...**

You have a lot of data, but no teaching data available – unsupervised learning you have at least an idea about the result Can be applied as a first approach to get some training data for supervised learning

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Applications Pattern recognition (text, numbers, faces): number plates, access at cash automata, Similarities between molecules Checking the quality of a surface Control of autonomous vehicles Monitoring of credit card accounts Data Mining

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**Applications Speech recognition Control of artificial limbs**

classification of galaxies Product orders (Supermarket) Forecast of energy consumption Stock value forecast

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**Application - Summary Classification Clustering Forecast**

Pattern recognition Learning by examples, generalization Recognition of not known structures in large data

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**Application Data Mining: Customer Data Weblog Control of ...**

Pattern Recognition Quality of surfaces possible if you have training data ...

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The End

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