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Lecturer(s)
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Course content
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Introduction, Biological motivations, characteristics of the human brain and its structure. An artificial neuron model, Perceptron. Artificial perceptron, teachings rules and gradient optimization. Adaptive Linear Perceptron, artificial neural networks, neural network architecture, topology of neural networks and their training (backpropagation- method of reverse return). Using neural networks for solving problems of classification, regression and prediction. "Soft Computing" method. Fuzzy Sets and operations with them, linguistic variables, membership functions, fuzzy logic, rules of inference, and Mamdami Larsen implicative rules, the number of rules fuzzyfication, defazzyfication. Implementation of technical practice and control.
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Learning activities and teaching methods
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Individual consultation
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Learning outcomes
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The aim of the course is to provide a doctoral student survey about methods of artificial intelligence and neural networks by solving the technical problems. The course also includes methods of fuzzy and "soft computing" methods.
The ability to implement elements of artificial intelligence and methods "Soft Computing".
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Prerequisites
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Successful graduation of magister study on faculty of mechanical engineering or on similar faculty, interest in measurement technique and in data evaluation.
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Assessment methods and criteria
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Oral exam
Knowledge of mathematics and physics demanded on graduates of faculty of mechanical engineering and basic knowledge of control principles.
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Recommended literature
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BROOKSHEAR, J. GLENN. Computer Science: An Overview (8th Edition). Addison Wesley, 2004. ISBN 0-321-26971-3.
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P. H. Winston. Artificial Intelligence. Addison Wesley, Reading, MA, 1992. ISBN 0-201-53377-4.
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Stuart RUSSELL, Peter NORVIG. Artificial Intelligence: A Modern Approach.. Prentice Hall International, Inc., New Jersey, 1995. ISBN 0-13-360124-2.
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Vladimír MAŘÍK, Olga ŠTEPÁNKOVÁ, Jiří LAŽANSKÝ a kolektiv. Umělá inteligence I. - V.díl. Academia Praha, 2007. ISBN 80-200-0502-1.
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