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Artificial Neural Network (Spring 2008) |
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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 biological nervous systems with emphasis on the human brain and its neurons. Artificial Neural Networks (ANNs) and their use:: • Adaline, perceptron, multilayer perceptrons • Linear models, estimation, regression, least square, LMS algorithm • Pattern recognition, classifiers, supervised and non supervised learning • Training algorithms, back propagation, adaptive systems • Non-linear models, perceptron, MLP, training, classification, error and stop criteria • Function approximation with MLP, Radial Basis Function networks, vector support machines • Hebbian learning, Oja´s rule, anti-Hebbian learning, Associative Memory, Winner-take-all networks, • Self Organising Maps and their use • Use of ANNs in digital signal processing, with emphasis on, adaptive filters • Static versus Dynamic systems, time-delay neural network • Training and using recurrent networks, feedback parameters, Hopfield networks, • Introduction to pulsed neural network and their electronic equivalents |
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| Teaching method : | The curriculum is based on an interactive book – overview lessons will allow (groups of) the students to study the interactive sessions and perform the related simulation exercises. Mat Lab examples and code will be used. | ||
| Required prequisites : | Math, analogue and digital design equivalent to 4.th semester level like DSM3/DSM4 | ||
| Responsible teacher : | |||