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Artificial Neural Network (Fall 2005) |
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| Course code : | EANN-U01 | ||
| ECTS Credits : | 7,5 | Status : | Optional |
| Placement : | 5-7 semester | Hours per week : | 4 |
| Length : | 1 semester | Teaching Language : | Danish if no English students are present |
| Principal Content : | Introduction to the principles of the human brain, neurons, dendrites, axom,…. Artificial Neural Network components: - Adaline, perceptron, multiplayer perceptrons - Linear models, estimation, regression, least square, LMS algorithm - Patterns recognition, classifiers, training parameters’ - Training algoritms, backpropagation,adaptive systems - Nonlinear models, perceptron, MLP, training, classification, error and stopcriterias - Function approximation with MLP, Radial Basis funcionc, vector support machines - Hebbian learning, Oja´s rule, anti-hebbian learning, Associate memory, winner-take-all network, adaptive resonance theory - Digital signal processing, time series, frequency domain, adaptive filters - Static versus Dynamic systems, time-delay neural network, gamma memory - Training and using recurrent network, feedback parameters, hopfield network, Grossbergs additive model - Introduction to pulsed neural network and related electronics |
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| Teaching method : | The curriculum is based on an interactive book – overview lessons will allow (groups of) the student to study the interactive sessions and perform the related simulation exercises. | ||
| Required prequisites : | Documented knowledge of math corresponding to DSM3/DSM4 Analogue or digital design equivalent to 4th semester level. |
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| Responsible teacher : | Kurt Jeritslev
, kuj@ihk.dk |
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