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4. Artificial Neural Networks

Objectives of this class:

• Functionality of a (artificial) neuron

• Neural networks (NN)

• Training of a NN

(2)

Universität Paderborn, ADT 3.2

Background, Definitions

●

An artificial neural network (ANN), often just called a "neural

network" (NN), is a mathematical model or computational model based on biological neural networks.

●

It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to

computation.

●

In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase.

●

In more practical terms neural networks are non-linear statistical data modeling tools.

●

NN can be used to model complex relationships between inputs and outputs or to find patterns in data.

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Functional Model of a Neuron (Brain Cell)

Synapse

Dendrite

Soma Axon

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Universität Paderborn, ADT 3.4

Technical Model of a Neuron

O

Schmitt Trigger +

–

a

Operational Amplifier (a: amplification factor) I1

I2 I3

(Additive)

I1 I2

I3

O

a Symbol of a Neuron

increasing decreasing

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Neuronal Network

I1 I2 I3

O1 O2

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